Analizowany zbiór danych posiada bardzo dużą liczbę klas (2696) oraz zmiennych (796). Analizę utrudnia również duża liczba wartości NA. Zbadanie korelacji występujących w zbiorze pokazuje że wiele kolumn niesie podobne informacje, co można wykorzystać w ograniczeniu kolumn użytych w regresji i klasyfikacji. W szczególności podobne są korelacje między kolumnami w ramach zbiorów part_XX zawierających wartości dla różnych progów odcięcia. Wyznaczenie najpopularniejszych klas w zbiorze pokazuje bardzo nierównomierny rozkład danych - po przefiltrowaniu danych tylko 18 klas posiada więcej niż 100 obserwacji. Z tego powodu stworzenie klasyfikatora uwzględniającego wszyskie klasy w praktyce nie ma sensu. Dosyć dokładnie możemy przewidywać tylko kilka najbardziej licznych klas. Dla najpopularniejszych 5 klas można uzyskać dokładnośc (accuracy) na poziomie 0.7. Zwiększając liczbę klas do 20 dokładność ulega istotnemu pogorszeniu i wynosi już tylko nieco ponad 0.55. Z przeprowadzonych eksperymetnów dotyczących predykcji liczby atomów i elektronów wynika, że predykcja taka jest w pewnym stopniu możliwa. W obu przypadkach uzyskano wartości miary R^2 na poziomie ok. 0.56
library(plyr)
library(knitr)
library(dplyr)
library(corrplot)
library(grid)
library(reshape)
library(ggplot2)
library(gridExtra)
library(Rmisc)
library(caret)
library(hydroGOF)
set.seed(23)
set.seed(23)
naString <- c("nan", "NA","NaN", " ")
data <- read.csv2('./all_summary.txt', na.strings = naString, dec='.')
allClasses <- sapply(data, class)
Wstępne przetworzenie danych rozpoczyna się od usunięcia wierszy posiadających określoną wartość zmiennej res_name. Następnie usuwane są wiersze tak aby pary (pdb_code, res_name) były unikalne.
removed_names <- c('DA','DC','DT', 'DU', 'DG', 'DI','UNK', 'UNX', 'UNL', 'PR', 'PD', 'Y1', 'EU', 'N', '15P', 'UQ', 'PX4', 'NAN')
data2 <- filter(data, !(res_name %in% removed_names))
dp <- duplicated(select(data2, pdb_code, res_name))
data3 <- data2[!dp, ]
kable(summary(data3))
| title | pdb_code | res_name | res_id | chain_id | local_BAa | local_NPa | local_Ra | local_RGa | local_SRGa | local_CCSa | local_CCPa | local_ZOa | local_ZDa | local_ZD_minus_a | local_ZD_plus_a | local_res_atom_count | local_res_atom_non_h_count | local_res_atom_non_h_occupancy_sum | local_res_atom_non_h_electron_sum | local_res_atom_non_h_electron_occupancy_sum | local_res_atom_C_count | local_res_atom_N_count | local_res_atom_O_count | local_res_atom_S_count | dict_atom_non_h_count | dict_atom_non_h_electron_sum | dict_atom_C_count | dict_atom_N_count | dict_atom_O_count | dict_atom_S_count | part_00_blob_electron_sum | part_00_blob_volume_sum | part_00_blob_parts | part_00_shape_O3 | part_00_shape_O4 | part_00_shape_O5 | part_00_shape_FL | part_00_shape_O3_norm | part_00_shape_O4_norm | part_00_shape_O5_norm | part_00_shape_FL_norm | part_00_shape_I1 | part_00_shape_I2 | part_00_shape_I3 | part_00_shape_I4 | part_00_shape_I5 | part_00_shape_I6 | part_00_shape_I1_norm | part_00_shape_I2_norm | part_00_shape_I3_norm | part_00_shape_I4_norm | part_00_shape_I5_norm | part_00_shape_I6_norm | part_00_shape_I1_scaled | part_00_shape_I2_scaled | part_00_shape_I3_scaled | part_00_shape_I4_scaled | part_00_shape_I5_scaled | part_00_shape_I6_scaled | part_00_shape_M000 | part_00_shape_E3_E1 | part_00_shape_E2_E1 | part_00_shape_E3_E2 | part_00_shape_sqrt_E1 | part_00_shape_sqrt_E2 | part_00_shape_sqrt_E3 | part_00_density_O3 | part_00_density_O4 | part_00_density_O5 | part_00_density_FL | part_00_density_O3_norm | part_00_density_O4_norm | part_00_density_O5_norm | part_00_density_FL_norm | part_00_density_I1 | part_00_density_I2 | part_00_density_I3 | part_00_density_I4 | part_00_density_I5 | part_00_density_I6 | part_00_density_I1_norm | part_00_density_I2_norm | part_00_density_I3_norm | part_00_density_I4_norm | part_00_density_I5_norm | part_00_density_I6_norm | part_00_density_I1_scaled | part_00_density_I2_scaled | part_00_density_I3_scaled | part_00_density_I4_scaled | part_00_density_I5_scaled | part_00_density_I6_scaled | part_00_density_M000 | part_00_density_E3_E1 | part_00_density_E2_E1 | part_00_density_E3_E2 | part_00_density_sqrt_E1 | part_00_density_sqrt_E2 | part_00_density_sqrt_E3 | part_01_blob_electron_sum | part_01_blob_volume_sum | part_01_blob_parts | part_01_shape_O3 | part_01_shape_O4 | part_01_shape_O5 | part_01_shape_FL | part_01_shape_O3_norm | part_01_shape_O4_norm | part_01_shape_O5_norm | part_01_shape_FL_norm | part_01_shape_I1 | part_01_shape_I2 | part_01_shape_I3 | part_01_shape_I4 | part_01_shape_I5 | part_01_shape_I6 | part_01_shape_I1_norm | part_01_shape_I2_norm | part_01_shape_I3_norm | part_01_shape_I4_norm | part_01_shape_I5_norm | part_01_shape_I6_norm | part_01_shape_I1_scaled | part_01_shape_I2_scaled | part_01_shape_I3_scaled | part_01_shape_I4_scaled | part_01_shape_I5_scaled | part_01_shape_I6_scaled | part_01_shape_M000 | part_01_shape_E3_E1 | part_01_shape_E2_E1 | part_01_shape_E3_E2 | part_01_shape_sqrt_E1 | part_01_shape_sqrt_E2 | part_01_shape_sqrt_E3 | part_01_density_O3 | part_01_density_O4 | part_01_density_O5 | part_01_density_FL | part_01_density_O3_norm | part_01_density_O4_norm | part_01_density_O5_norm | part_01_density_FL_norm | part_01_density_I1 | part_01_density_I2 | part_01_density_I3 | part_01_density_I4 | part_01_density_I5 | part_01_density_I6 | part_01_density_I1_norm | part_01_density_I2_norm | part_01_density_I3_norm | part_01_density_I4_norm | part_01_density_I5_norm | part_01_density_I6_norm | part_01_density_I1_scaled | part_01_density_I2_scaled | part_01_density_I3_scaled | part_01_density_I4_scaled | part_01_density_I5_scaled | part_01_density_I6_scaled | part_01_density_M000 | part_01_density_E3_E1 | part_01_density_E2_E1 | part_01_density_E3_E2 | part_01_density_sqrt_E1 | part_01_density_sqrt_E2 | part_01_density_sqrt_E3 | part_02_blob_electron_sum | part_02_blob_volume_sum | part_02_blob_parts | part_02_shape_O3 | part_02_shape_O4 | part_02_shape_O5 | part_02_shape_FL | part_02_shape_O3_norm | part_02_shape_O4_norm | part_02_shape_O5_norm | part_02_shape_FL_norm | part_02_shape_I1 | part_02_shape_I2 | part_02_shape_I3 | part_02_shape_I4 | part_02_shape_I5 | part_02_shape_I6 | part_02_shape_I1_norm | part_02_shape_I2_norm | part_02_shape_I3_norm | part_02_shape_I4_norm | part_02_shape_I5_norm | part_02_shape_I6_norm | part_02_shape_I1_scaled | part_02_shape_I2_scaled | part_02_shape_I3_scaled | part_02_shape_I4_scaled | part_02_shape_I5_scaled | part_02_shape_I6_scaled | part_02_shape_M000 | part_02_shape_E3_E1 | part_02_shape_E2_E1 | part_02_shape_E3_E2 | part_02_shape_sqrt_E1 | part_02_shape_sqrt_E2 | part_02_shape_sqrt_E3 | part_02_density_O3 | part_02_density_O4 | part_02_density_O5 | part_02_density_FL | part_02_density_O3_norm | part_02_density_O4_norm | part_02_density_O5_norm | part_02_density_FL_norm | part_02_density_I1 | part_02_density_I2 | part_02_density_I3 | part_02_density_I4 | part_02_density_I5 | part_02_density_I6 | part_02_density_I1_norm | part_02_density_I2_norm | part_02_density_I3_norm | part_02_density_I4_norm | part_02_density_I5_norm | part_02_density_I6_norm | part_02_density_I1_scaled | part_02_density_I2_scaled | part_02_density_I3_scaled | part_02_density_I4_scaled | part_02_density_I5_scaled | part_02_density_I6_scaled | part_02_density_M000 | part_02_density_E3_E1 | part_02_density_E2_E1 | part_02_density_E3_E2 | part_02_density_sqrt_E1 | part_02_density_sqrt_E2 | part_02_density_sqrt_E3 | part_03_blob_electron_sum | part_03_blob_volume_sum | part_03_blob_parts | part_03_shape_O3 | part_03_shape_O4 | part_03_shape_O5 | part_03_shape_FL | part_03_shape_O3_norm | part_03_shape_O4_norm | part_03_shape_O5_norm | part_03_shape_FL_norm | part_03_shape_I1 | part_03_shape_I2 | part_03_shape_I3 | part_03_shape_I4 | part_03_shape_I5 | part_03_shape_I6 | part_03_shape_I1_norm | part_03_shape_I2_norm | part_03_shape_I3_norm | part_03_shape_I4_norm | part_03_shape_I5_norm | part_03_shape_I6_norm | part_03_shape_I1_scaled | part_03_shape_I2_scaled | part_03_shape_I3_scaled | part_03_shape_I4_scaled | part_03_shape_I5_scaled | part_03_shape_I6_scaled | part_03_shape_M000 | part_03_shape_E3_E1 | part_03_shape_E2_E1 | part_03_shape_E3_E2 | part_03_shape_sqrt_E1 | part_03_shape_sqrt_E2 | part_03_shape_sqrt_E3 | part_03_density_O3 | part_03_density_O4 | part_03_density_O5 | part_03_density_FL | part_03_density_O3_norm | part_03_density_O4_norm | part_03_density_O5_norm | part_03_density_FL_norm | part_03_density_I1 | part_03_density_I2 | part_03_density_I3 | part_03_density_I4 | part_03_density_I5 | part_03_density_I6 | part_03_density_I1_norm | part_03_density_I2_norm | part_03_density_I3_norm | part_03_density_I4_norm | part_03_density_I5_norm | part_03_density_I6_norm | part_03_density_I1_scaled | part_03_density_I2_scaled | part_03_density_I3_scaled | part_03_density_I4_scaled | part_03_density_I5_scaled | part_03_density_I6_scaled | part_03_density_M000 | part_03_density_E3_E1 | part_03_density_E2_E1 | part_03_density_E3_E2 | part_03_density_sqrt_E1 | part_03_density_sqrt_E2 | part_03_density_sqrt_E3 | part_04_blob_electron_sum | part_04_blob_volume_sum | part_04_blob_parts | part_04_shape_O3 | part_04_shape_O4 | part_04_shape_O5 | part_04_shape_FL | part_04_shape_O3_norm | part_04_shape_O4_norm | part_04_shape_O5_norm | part_04_shape_FL_norm | part_04_shape_I1 | part_04_shape_I2 | part_04_shape_I3 | part_04_shape_I4 | part_04_shape_I5 | part_04_shape_I6 | part_04_shape_I1_norm | part_04_shape_I2_norm | part_04_shape_I3_norm | part_04_shape_I4_norm | part_04_shape_I5_norm | part_04_shape_I6_norm | part_04_shape_I1_scaled | part_04_shape_I2_scaled | part_04_shape_I3_scaled | part_04_shape_I4_scaled | part_04_shape_I5_scaled | part_04_shape_I6_scaled | part_04_shape_M000 | part_04_shape_E3_E1 | part_04_shape_E2_E1 | part_04_shape_E3_E2 | part_04_shape_sqrt_E1 | part_04_shape_sqrt_E2 | part_04_shape_sqrt_E3 | part_04_density_O3 | part_04_density_O4 | part_04_density_O5 | part_04_density_FL | part_04_density_O3_norm | part_04_density_O4_norm | part_04_density_O5_norm | part_04_density_FL_norm | part_04_density_I1 | part_04_density_I2 | part_04_density_I3 | part_04_density_I4 | part_04_density_I5 | part_04_density_I6 | part_04_density_I1_norm | part_04_density_I2_norm | part_04_density_I3_norm | part_04_density_I4_norm | part_04_density_I5_norm | part_04_density_I6_norm | part_04_density_I1_scaled | part_04_density_I2_scaled | part_04_density_I3_scaled | part_04_density_I4_scaled | part_04_density_I5_scaled | part_04_density_I6_scaled | part_04_density_M000 | part_04_density_E3_E1 | part_04_density_E2_E1 | part_04_density_E3_E2 | part_04_density_sqrt_E1 | part_04_density_sqrt_E2 | part_04_density_sqrt_E3 | part_05_blob_electron_sum | part_05_blob_volume_sum | part_05_blob_parts | part_05_shape_O3 | part_05_shape_O4 | part_05_shape_O5 | part_05_shape_FL | part_05_shape_O3_norm | part_05_shape_O4_norm | part_05_shape_O5_norm | part_05_shape_FL_norm | part_05_shape_I1 | part_05_shape_I2 | part_05_shape_I3 | part_05_shape_I4 | part_05_shape_I5 | part_05_shape_I6 | part_05_shape_I1_norm | part_05_shape_I2_norm | part_05_shape_I3_norm | part_05_shape_I4_norm | part_05_shape_I5_norm | part_05_shape_I6_norm | part_05_shape_I1_scaled | part_05_shape_I2_scaled | part_05_shape_I3_scaled | part_05_shape_I4_scaled | part_05_shape_I5_scaled | part_05_shape_I6_scaled | part_05_shape_M000 | part_05_shape_E3_E1 | part_05_shape_E2_E1 | part_05_shape_E3_E2 | part_05_shape_sqrt_E1 | part_05_shape_sqrt_E2 | part_05_shape_sqrt_E3 | part_05_density_O3 | part_05_density_O4 | part_05_density_O5 | part_05_density_FL | part_05_density_O3_norm | part_05_density_O4_norm | part_05_density_O5_norm | part_05_density_FL_norm | part_05_density_I1 | part_05_density_I2 | part_05_density_I3 | part_05_density_I4 | part_05_density_I5 | part_05_density_I6 | part_05_density_I1_norm | part_05_density_I2_norm | part_05_density_I3_norm | part_05_density_I4_norm | part_05_density_I5_norm | part_05_density_I6_norm | part_05_density_I1_scaled | part_05_density_I2_scaled | part_05_density_I3_scaled | part_05_density_I4_scaled | part_05_density_I5_scaled | part_05_density_I6_scaled | part_05_density_M000 | part_05_density_E3_E1 | part_05_density_E2_E1 | part_05_density_E3_E2 | part_05_density_sqrt_E1 | part_05_density_sqrt_E2 | part_05_density_sqrt_E3 | part_06_blob_electron_sum | part_06_blob_volume_sum | part_06_blob_parts | part_06_shape_O3 | part_06_shape_O4 | part_06_shape_O5 | part_06_shape_FL | part_06_shape_O3_norm | part_06_shape_O4_norm | part_06_shape_O5_norm | part_06_shape_FL_norm | part_06_shape_I1 | part_06_shape_I2 | part_06_shape_I3 | part_06_shape_I4 | part_06_shape_I5 | part_06_shape_I6 | part_06_shape_I1_norm | part_06_shape_I2_norm | part_06_shape_I3_norm | part_06_shape_I4_norm | part_06_shape_I5_norm | part_06_shape_I6_norm | part_06_shape_I1_scaled | part_06_shape_I2_scaled | part_06_shape_I3_scaled | part_06_shape_I4_scaled | part_06_shape_I5_scaled | part_06_shape_I6_scaled | part_06_shape_M000 | part_06_shape_E3_E1 | part_06_shape_E2_E1 | part_06_shape_E3_E2 | part_06_shape_sqrt_E1 | part_06_shape_sqrt_E2 | part_06_shape_sqrt_E3 | part_06_density_O3 | part_06_density_O4 | part_06_density_O5 | part_06_density_FL | part_06_density_O3_norm | part_06_density_O4_norm | part_06_density_O5_norm | part_06_density_FL_norm | part_06_density_I1 | part_06_density_I2 | part_06_density_I3 | part_06_density_I4 | part_06_density_I5 | part_06_density_I6 | part_06_density_I1_norm | part_06_density_I2_norm | part_06_density_I3_norm | part_06_density_I4_norm | part_06_density_I5_norm | part_06_density_I6_norm | part_06_density_I1_scaled | part_06_density_I2_scaled | part_06_density_I3_scaled | part_06_density_I4_scaled | part_06_density_I5_scaled | part_06_density_I6_scaled | part_06_density_M000 | part_06_density_E3_E1 | part_06_density_E2_E1 | part_06_density_E3_E2 | part_06_density_sqrt_E1 | part_06_density_sqrt_E2 | part_06_density_sqrt_E3 | part_07_blob_electron_sum | part_07_blob_volume_sum | part_07_blob_parts | part_07_shape_O3 | part_07_shape_O4 | part_07_shape_O5 | part_07_shape_FL | part_07_shape_O3_norm | part_07_shape_O4_norm | part_07_shape_O5_norm | part_07_shape_FL_norm | part_07_shape_I1 | part_07_shape_I2 | part_07_shape_I3 | part_07_shape_I4 | part_07_shape_I5 | part_07_shape_I6 | part_07_shape_I1_norm | part_07_shape_I2_norm | part_07_shape_I3_norm | part_07_shape_I4_norm | part_07_shape_I5_norm | part_07_shape_I6_norm | part_07_shape_I1_scaled | part_07_shape_I2_scaled | part_07_shape_I3_scaled | part_07_shape_I4_scaled | part_07_shape_I5_scaled | part_07_shape_I6_scaled | part_07_shape_M000 | part_07_shape_E3_E1 | part_07_shape_E2_E1 | part_07_shape_E3_E2 | part_07_shape_sqrt_E1 | part_07_shape_sqrt_E2 | part_07_shape_sqrt_E3 | part_07_density_O3 | part_07_density_O4 | part_07_density_O5 | part_07_density_FL | part_07_density_O3_norm | part_07_density_O4_norm | part_07_density_O5_norm | part_07_density_FL_norm | part_07_density_I1 | part_07_density_I2 | part_07_density_I3 | part_07_density_I4 | part_07_density_I5 | part_07_density_I6 | part_07_density_I1_norm | part_07_density_I2_norm | part_07_density_I3_norm | part_07_density_I4_norm | part_07_density_I5_norm | part_07_density_I6_norm | part_07_density_I1_scaled | part_07_density_I2_scaled | part_07_density_I3_scaled | part_07_density_I4_scaled | part_07_density_I5_scaled | part_07_density_I6_scaled | part_07_density_M000 | part_07_density_E3_E1 | part_07_density_E2_E1 | part_07_density_E3_E2 | part_07_density_sqrt_E1 | part_07_density_sqrt_E2 | part_07_density_sqrt_E3 | part_08_blob_electron_sum | part_08_blob_volume_sum | part_08_blob_parts | part_08_shape_O3 | part_08_shape_O4 | part_08_shape_O5 | part_08_shape_FL | part_08_shape_O3_norm | part_08_shape_O4_norm | part_08_shape_O5_norm | part_08_shape_FL_norm | part_08_shape_I1 | part_08_shape_I2 | part_08_shape_I3 | part_08_shape_I4 | part_08_shape_I5 | part_08_shape_I6 | part_08_shape_I1_norm | part_08_shape_I2_norm | part_08_shape_I3_norm | part_08_shape_I4_norm | part_08_shape_I5_norm | part_08_shape_I6_norm | part_08_shape_I1_scaled | part_08_shape_I2_scaled | part_08_shape_I3_scaled | part_08_shape_I4_scaled | part_08_shape_I5_scaled | part_08_shape_I6_scaled | part_08_shape_M000 | part_08_shape_E3_E1 | part_08_shape_E2_E1 | part_08_shape_E3_E2 | part_08_shape_sqrt_E1 | part_08_shape_sqrt_E2 | part_08_shape_sqrt_E3 | part_08_density_O3 | part_08_density_O4 | part_08_density_O5 | part_08_density_FL | part_08_density_O3_norm | part_08_density_O4_norm | part_08_density_O5_norm | part_08_density_FL_norm | part_08_density_I1 | part_08_density_I2 | part_08_density_I3 | part_08_density_I4 | part_08_density_I5 | part_08_density_I6 | part_08_density_I1_norm | part_08_density_I2_norm | part_08_density_I3_norm | part_08_density_I4_norm | part_08_density_I5_norm | part_08_density_I6_norm | part_08_density_I1_scaled | part_08_density_I2_scaled | part_08_density_I3_scaled | part_08_density_I4_scaled | part_08_density_I5_scaled | part_08_density_I6_scaled | part_08_density_M000 | part_08_density_E3_E1 | part_08_density_E2_E1 | part_08_density_E3_E2 | part_08_density_sqrt_E1 | part_08_density_sqrt_E2 | part_08_density_sqrt_E3 | part_09_blob_electron_sum | part_09_blob_volume_sum | part_09_blob_parts | part_09_shape_O3 | part_09_shape_O4 | part_09_shape_O5 | part_09_shape_FL | part_09_shape_O3_norm | part_09_shape_O4_norm | part_09_shape_O5_norm | part_09_shape_FL_norm | part_09_shape_I1 | part_09_shape_I2 | part_09_shape_I3 | part_09_shape_I4 | part_09_shape_I5 | part_09_shape_I6 | part_09_shape_I1_norm | part_09_shape_I2_norm | part_09_shape_I3_norm | part_09_shape_I4_norm | part_09_shape_I5_norm | part_09_shape_I6_norm | part_09_shape_I1_scaled | part_09_shape_I2_scaled | part_09_shape_I3_scaled | part_09_shape_I4_scaled | part_09_shape_I5_scaled | part_09_shape_I6_scaled | part_09_shape_M000 | part_09_shape_E3_E1 | part_09_shape_E2_E1 | part_09_shape_E3_E2 | part_09_shape_sqrt_E1 | part_09_shape_sqrt_E2 | part_09_shape_sqrt_E3 | part_09_density_O3 | part_09_density_O4 | part_09_density_O5 | part_09_density_FL | part_09_density_O3_norm | part_09_density_O4_norm | part_09_density_O5_norm | part_09_density_FL_norm | part_09_density_I1 | part_09_density_I2 | part_09_density_I3 | part_09_density_I4 | part_09_density_I5 | part_09_density_I6 | part_09_density_I1_norm | part_09_density_I2_norm | part_09_density_I3_norm | part_09_density_I4_norm | part_09_density_I5_norm | part_09_density_I6_norm | part_09_density_I1_scaled | part_09_density_I2_scaled | part_09_density_I3_scaled | part_09_density_I4_scaled | part_09_density_I5_scaled | part_09_density_I6_scaled | part_09_density_M000 | part_09_density_E3_E1 | part_09_density_E2_E1 | part_09_density_E3_E2 | part_09_density_sqrt_E1 | part_09_density_sqrt_E2 | part_09_density_sqrt_E3 | local_volume | local_electrons | local_mean | local_std | local_min | local_max | local_skewness | local_parts | fo_col | fc_col | weight_col | grid_space | solvent_radius | solvent_opening_radius | resolution_max_limit | resolution | TwoFoFc_mean | TwoFoFc_std | TwoFoFc_square_std | TwoFoFc_min | TwoFoFc_max | Fo_mean | Fo_std | Fo_square_std | Fo_min | Fo_max | FoFc_mean | FoFc_std | FoFc_square_std | FoFc_min | FoFc_max | Fc_mean | Fc_std | Fc_square_std | Fc_min | Fc_max | solvent_mask_count | void_mask_count | modeled_mask_count | solvent_ratio | TwoFoFc_bulk_mean | TwoFoFc_bulk_std | TwoFoFc_void_mean | TwoFoFc_void_std | TwoFoFc_modeled_mean | TwoFoFc_modeled_std | Fo_bulk_mean | Fo_bulk_std | Fo_void_mean | Fo_void_std | Fo_modeled_mean | Fo_modeled_std | FoFc_bulk_mean | FoFc_bulk_std | FoFc_void_mean | FoFc_void_std | FoFc_modeled_mean | FoFc_modeled_std | Fc_bulk_mean | Fc_bulk_std | Fc_void_mean | Fc_void_std | Fc_modeled_mean | Fc_modeled_std | TwoFoFc_void_fit_binormal_mean1 | TwoFoFc_void_fit_binormal_std1 | TwoFoFc_void_fit_binormal_mean2 | TwoFoFc_void_fit_binormal_std2 | TwoFoFc_void_fit_binormal_scale | TwoFoFc_solvent_fit_normal_mean | TwoFoFc_solvent_fit_normal_std | part_step_FoFc_std_min | part_step_FoFc_std_max | part_step_FoFc_std_step | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110l BME 901 A: 1 | 3ag4 : 16 | SO4 : 1183 | 301 : 430 | A :8630 | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Mode:logical | Min. : 1.00 | Min. : 1.00 | Min. : 0.00 | Min. : 3.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. :0.0000 | Min. : 1.00 | Min. : 3.0 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 0.000 | Min. : 0.00 | Min. :0.0000 | Min. : 24253 | Min. :1.952e+08 | Min. :5.160e+11 | Min. :3.330e+06 | Min. :0.2312 | Min. :0.0178 | Min. :0.0005 | Min. :0.0000 | Min. :6.875e+05 | Min. :1.246e+11 | Min. :9.688e+10 | Min. :1.335e+06 | Min. :4.728e+03 | Min. :5.618e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. :0.0000 | Min. :0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0e+00 | Min. :0 | Min. : 1023 | Min. :0.0005 | Min. :0.0142 | Min. :0.0042 | Min. : 2.930 | Min. : 1.793 | Min. : 0.498 | Min. : 1527 | Min. :7.096e+05 | Min. :1.011e+08 | Min. :9.595e+05 | Min. :0.0593 | Min. :0.0012 | Min. :0.0000 | Min. : 0.0000 | Min. :6.548e+04 | Min. :9.611e+08 | Min. :1.275e+09 | Min. :4.130e+05 | Min. :2.366e+04 | Min. :3.899e+07 | Min. : 0.0048 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 47 | Min. :0.0007 | Min. :0.0133 | Min. :0.0052 | Min. : 2.499 | Min. : 1.769 | Min. : 0.4972 | Min. : 0.000 | Min. : 0.00 | Min. :0.0000 | Min. : 24186 | Min. :1.947e+08 | Min. :5.217e+11 | Min. :2.023e+06 | Min. :0.2313 | Min. :0.0178 | Min. :0.0005 | Min. :0.0000 | Min. :6.807e+05 | Min. :1.231e+11 | Min. :9.354e+10 | Min. :8.184e+05 | Min. :3.360e+03 | Min. :5.502e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. :0.0000 | Min. :0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.000 | Min. :0.0000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.0005 | Min. :0.0127 | Min. :0.0044 | Min. : 2.898 | Min. : 1.947 | Min. : 0.4978 | Min. : 1038 | Min. :3.280e+05 | Min. :3.182e+07 | Min. :3.230e+05 | Min. :0.0592 | Min. :0.0012 | Min. :0.0000 | Min. : 0.0000 | Min. :3.748e+04 | Min. :3.151e+08 | Min. :4.195e+08 | Min. :1.414e+05 | Min. :1.786e+04 | Min. :1.512e+07 | Min. : 0.0048 | Min. : 0.0000 | Min. : 0.000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 37.76 | Min. :0.0007 | Min. :0.0122 | Min. :0.0054 | Min. : 2.496 | Min. : 1.877 | Min. : 0.4971 | Min. : 0.00 | Min. : 0.000 | Min. :0.0000 | Min. : 24331 | Min. :1.955e+08 | Min. :5.180e+11 | Min. :2.142e+06 | Min. :0.2312 | Min. :0.0178 | Min. :0.0005 | Min. : 0.0000 | Min. :6.937e+05 | Min. :1.254e+11 | Min. :1.013e+11 | Min. :8.662e+05 | Min. :3.121e+03 | Min. :5.751e+09 | Min. : 0.0637 | Min. : 0.0011 | Min. : 0.0008 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0049 | Min. :0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0 | Min. : 1023 | Min. :0.0006 | Min. :0.0138 | Min. :0.0047 | Min. : 2.917 | Min. : 1.914 | Min. : 0.4957 | Min. : 4269 | Min. :5.654e+06 | Min. :2.385e+09 | Min. :1.443e+06 | Min. :0.0588 | Min. :0.0011 | Min. :0.0000 | Min. : 0.0000 | Min. :1.324e+05 | Min. :3.851e+09 | Min. :4.897e+09 | Min. :6.988e+05 | Min. :1.294e+04 | Min. :2.080e+08 | Min. : 0.0047 | Min. : 0.0000 | Min. : 0.000 | Min. : 0.0000 | Min. : 0.0000 | Min. : 0.0001 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. :0.0000 | Min. : 177.5 | Min. :0.0008 | Min. :0.0138 | Min. :0.0058 | Min. : 2.487 | Min. : 1.857 | Min. : 0.4957 | Min. : 0.0 | Min. : 0.00 | Min. :0.0000 | Min. : 24164 | Min. :1.941e+08 | Min. :5.143e+11 | Min. :6.027e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.790e+05 | Min. :1.225e+11 | Min. :9.315e+10 | Min. :2.433e+06 | Min. :1.301e+03 | Min. :5.484e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.012 | Min. :0.005 | Min. : 2.884 | Min. : 1.817 | Min. : 0.493 | Min. : 4027 | Min. :5.262e+06 | Min. :2.243e+09 | Min. :1.389e+06 | Min. : 0.058 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.186e+05 | Min. :3.489e+09 | Min. :3.239e+09 | Min. :5.853e+05 | Min. :1.301e+04 | Min. :1.669e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. : 0.000 | Min. :0.00 | Min. :0.000 | Min. :0.000 | Min. : 168 | Min. :0.001 | Min. :0.011 | Min. :0.006 | Min. : 2.473 | Min. : 1.773 | Min. : 0.494 | Min. : 0.000 | Min. : 0.0 | Min. :0.0000 | Min. : 24198 | Min. :1.946e+08 | Min. :5.198e+11 | Min. :7.913e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.817e+05 | Min. :1.231e+11 | Min. :9.400e+10 | Min. :3.183e+06 | Min. :3.774e+03 | Min. :5.528e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.009 | Min. :0.010 | Min. : 2.869 | Min. : 1.644 | Min. : 0.496 | Min. : 6632 | Min. :1.333e+07 | Min. :7.807e+09 | Min. :1.426e+06 | Min. : 0.058 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.208e+05 | Min. :1.088e+10 | Min. :1.331e+10 | Min. :5.746e+05 | Min. :3.000e+00 | Min. :5.871e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 260.4 | Min. :0.001 | Min. :0.008 | Min. :0.011 | Min. : 2.577 | Min. : 1.617 | Min. : 0.497 | Min. : 0.000 | Min. : 0.000 | Min. :0.00 | Min. : 24236 | Min. :1.956e+08 | Min. :5.224e+11 | Min. :4.696e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.861e+05 | Min. :1.254e+11 | Min. :9.477e+10 | Min. :1.895e+06 | Min. :5.507e+03 | Min. :5.553e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.008 | Min. :0.011 | Min. : 2.866 | Min. : 2.056 | Min. : 0.496 | Min. : 6155 | Min. :1.161e+07 | Min. :6.600e+09 | Min. :8.006e+05 | Min. : 0.058 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.955e+05 | Min. :9.552e+09 | Min. :8.451e+09 | Min. :3.227e+05 | Min. :8.210e+02 | Min. :4.207e+08 | Min. : 0.005 | Min. : 0.000 | Min. : 0.0 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 234.8 | Min. :0.001 | Min. :0.008 | Min. :0.013 | Min. : 2.538 | Min. : 1.980 | Min. : 0.496 | Min. : 0.00 | Min. : 0.00 | Min. :0.0000 | Min. : 24358 | Min. :1.971e+08 | Min. :5.177e+11 | Min. :7.396e+06 | Min. : 0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.850e+05 | Min. :1.250e+11 | Min. :9.408e+10 | Min. :2.975e+06 | Min. :4.763e+03 | Min. :5.566e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.001 | Min. :0.008 | Min. :0.012 | Min. : 2.853 | Min. : 2.037 | Min. : 0.496 | Min. : 6900 | Min. :1.522e+07 | Min. :1.075e+10 | Min. :2.231e+06 | Min. : 0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.044e+05 | Min. :1.033e+10 | Min. :1.034e+10 | Min. :8.939e+05 | Min. :2.507e+03 | Min. :5.106e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.00 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0.000 | Min. : 292.1 | Min. :0.001 | Min. :0.007 | Min. :0.014 | Min. : 2.523 | Min. : 1.955 | Min. : 0.496 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24313 | Min. :1.960e+08 | Min. :5.149e+11 | Min. :5.901e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.911e+05 | Min. :1.255e+11 | Min. :9.856e+10 | Min. :2.436e+06 | Min. :2.860e+03 | Min. :5.679e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.006 | Min. :0.007 | Min. :0.018 | Min. : 2.930 | Min. : 1.947 | Min. : 1.077 | Min. : 7722 | Min. :1.917e+07 | Min. :1.546e+10 | Min. :2.261e+06 | Min. :0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.348e+05 | Min. :1.442e+10 | Min. :1.161e+10 | Min. :9.168e+05 | Min. :6.445e+03 | Min. :6.453e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 291.9 | Min. :0.005 | Min. :0.007 | Min. :0.018 | Min. : 2.610 | Min. : 1.921 | Min. : 1.063 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24284 | Min. :1.955e+08 | Min. :5.167e+11 | Min. :4.143e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :6.883e+05 | Min. :1.246e+11 | Min. :9.780e+10 | Min. :1.767e+06 | Min. :5.470e+02 | Min. :5.625e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0 | Min. : 1023 | Min. :0.005 | Min. :0.010 | Min. :0.020 | Min. : 2.942 | Min. : 2.154 | Min. : 1.051 | Min. : 6023 | Min. :1.180e+07 | Min. :7.555e+09 | Min. :1.376e+06 | Min. :0.057 | Min. :0.001 | Min. :0.000 | Min. : 0.000 | Min. :1.750e+05 | Min. :7.599e+09 | Min. :7.022e+09 | Min. :5.570e+05 | Min. :8.525e+03 | Min. :3.736e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0 | Min. : 256.5 | Min. :0.004 | Min. :0.010 | Min. :0.020 | Min. : 2.620 | Min. : 2.106 | Min. : 1.041 | Min. : 0.000 | Min. : 0.000 | Min. :0.0000 | Min. : 24584 | Min. :1.970e+08 | Min. :5.129e+11 | Min. :4.052e+06 | Min. :0.231 | Min. :0.018 | Min. :0.000 | Min. : 0.000 | Min. :7.117e+05 | Min. :1.303e+11 | Min. :1.047e+11 | Min. :1.669e+06 | Min. :3.899e+03 | Min. :5.953e+09 | Min. : 0.064 | Min. : 0.001 | Min. : 0.001 | Min. : 0.000 | Min. : 0.000 | Min. : 0.005 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.00 | Min. :0 | Min. : 1023 | Min. :0.004 | Min. :0.011 | Min. :0.025 | Min. : 2.949 | Min. : 2.144 | Min. : 1.060 | Min. : 7987 | Min. :2.102e+07 | Min. :1.388e+10 | Min. :1.586e+06 | Min. : 0.056 | Min. : 0.001 | Min. :0.000 | Min. : 0.000 | Min. :2.621e+05 | Min. :1.642e+10 | Min. :1.529e+10 | Min. :6.442e+05 | Min. :1.606e+04 | Min. :7.249e+08 | Min. : 0.004 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. : 0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. :0.000 | Min. : 297.4 | Min. :0.004 | Min. :0.010 | Min. :0.025 | Min. : 2.598 | Min. : 2.108 | Min. : 1.045 | Min. : 64.77 | Min. : 0.000 | Min. :0.00000 | Min. :0.0000 | Min. :0 | Min. : 0.000 | Min. :0.0000 | Min. :0.0000 | DELFWT:14132 | PHDELWT:14132 | Mode:logical | Min. :0.2 | Min. :1.9 | Min. :1.4 | Min. :2 | Min. :0.800 | Min. :-2.992e-06 | Min. :0.009962 | Min. :0.0003424 | Min. :-2.3692 | Min. : 0.2245 | Min. :-3.028e-06 | Min. :0.01847 | Min. :0.003589 | Min. :-3.3935 | Min. : 0.2999 | Min. :-2.206e-06 | Min. :0.009009 | Min. :0.0001201 | Min. :-26.24490 | Min. : 0.07625 | Min. :-7.867e-07 | Min. :0.005332 | Min. :0.000403 | Min. :-3.2550 | Min. : 0.2407 | Min. : 0 | Min. : 5472 | Min. : 15008 | Min. :0.0000 | Min. :-0.30666 | Min. :0.00930 | Min. :-0.2850280 | Min. :0.02783 | Min. :6.595e-05 | Min. :0.03485 | Min. :-0.08705 | Min. :0.01120 | Min. :-0.24222 | Min. :0.03827 | Min. :-0.003379 | Min. :0.05494 | Min. :-0.08982 | Min. :0.00893 | Min. :-0.095296 | Min. :0.01336 | Min. :-0.11559 | Min. :0.01705 | Min. :-0.21683 | Min. :0.00345 | Min. :-0.239831 | Min. :0.01529 | Min. :-0.005495 | Min. :0.02092 | Min. :-0.66520 | Min. :0.01075 | Min. :0.000e+00 | Min. :0.000e+00 | Min. :-11.2668 | Min. :-0.31511 | Min. :0.00918 | Min. :2.5 | Min. :7.1 | Min. :0.5 | |
| 111l CL 173 A : 1 | 3abl : 15 | ZN : 849 | 1 : 424 | B :2905 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | NA’s:14132 | 1st Qu.: 1.00 | 1st Qu.: 1.00 | 1st Qu.: 1.00 | 1st Qu.: 30.0 | 1st Qu.: 30.00 | 1st Qu.: 0.000 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 1.00 | 1st Qu.: 30.0 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 9.469 | 1st Qu.: 14.44 | 1st Qu.:1.0000 | 1st Qu.: 106958 | 1st Qu.:2.876e+09 | 1st Qu.:2.121e+13 | 1st Qu.:4.427e+10 | 1st Qu.:0.3173 | 1st Qu.:0.0275 | 1st Qu.:0.0007 | 1st Qu.:0.0021 | 1st Qu.:8.630e+06 | 1st Qu.:1.181e+13 | 1st Qu.:2.797e+13 | 1st Qu.:2.340e+10 | 1st Qu.:5.138e+09 | 1st Qu.:4.161e+11 | 1st Qu.: 0.1478 | 1st Qu.: 0.0037 | 1st Qu.: 0.0082 | 1st Qu.:0.0011 | 1st Qu.:0.0002 | 1st Qu.: 0.0202 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0e+00 | 1st Qu.:0 | 1st Qu.: 1900 | 1st Qu.:0.0715 | 1st Qu.:0.1974 | 1st Qu.:0.3046 | 1st Qu.: 5.594 | 1st Qu.: 3.546 | 1st Qu.: 2.654 | 1st Qu.: 62839 | 1st Qu.:1.048e+09 | 1st Qu.:4.744e+12 | 1st Qu.:1.340e+10 | 1st Qu.:0.3736 | 1st Qu.:0.0376 | 1st Qu.:0.0011 | 1st Qu.: 0.0033 | 1st Qu.:4.534e+06 | 1st Qu.:3.276e+12 | 1st Qu.:7.603e+12 | 1st Qu.:7.514e+09 | 1st Qu.:2.054e+09 | 1st Qu.:1.222e+11 | 1st Qu.: 0.2201 | 1st Qu.: 0.0084 | 1st Qu.: 0.0169 | 1st Qu.: 0.0020 | 1st Qu.: 0.0006 | 1st Qu.: 0.0343 | 1st Qu.:0.0014 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1263 | 1st Qu.:0.0678 | 1st Qu.:0.1912 | 1st Qu.:0.2997 | 1st Qu.: 5.086 | 1st Qu.: 3.263 | 1st Qu.: 2.4659 | 1st Qu.: 8.581 | 1st Qu.: 12.77 | 1st Qu.:1.0000 | 1st Qu.: 99934 | 1st Qu.:2.480e+09 | 1st Qu.:1.702e+13 | 1st Qu.:3.533e+10 | 1st Qu.:0.3104 | 1st Qu.:0.0267 | 1st Qu.:0.0007 | 1st Qu.:0.0019 | 1st Qu.:7.759e+06 | 1st Qu.:9.470e+12 | 1st Qu.:2.146e+13 | 1st Qu.:1.857e+10 | 1st Qu.:3.874e+09 | 1st Qu.:3.399e+11 | 1st Qu.: 0.1405 | 1st Qu.: 0.0034 | 1st Qu.: 0.0072 | 1st Qu.:0.0010 | 1st Qu.:0.0002 | 1st Qu.: 0.0184 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.000 | 1st Qu.:0.0000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1829 | 1st Qu.:0.0687 | 1st Qu.:0.1929 | 1st Qu.:0.2994 | 1st Qu.: 5.444 | 1st Qu.: 3.499 | 1st Qu.: 2.6289 | 1st Qu.: 61609 | 1st Qu.:1.002e+09 | 1st Qu.:4.474e+12 | 1st Qu.:1.122e+10 | 1st Qu.:0.3578 | 1st Qu.:0.0353 | 1st Qu.:0.0010 | 1st Qu.: 0.0027 | 1st Qu.:4.245e+06 | 1st Qu.:2.887e+12 | 1st Qu.:6.328e+12 | 1st Qu.:6.274e+09 | 1st Qu.:1.645e+09 | 1st Qu.:1.122e+11 | 1st Qu.: 0.1991 | 1st Qu.: 0.0072 | 1st Qu.: 0.014 | 1st Qu.: 0.0015 | 1st Qu.: 0.0004 | 1st Qu.: 0.0295 | 1st Qu.:0.0013 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1279.76 | 1st Qu.:0.0653 | 1st Qu.:0.1865 | 1st Qu.:0.2938 | 1st Qu.: 4.912 | 1st Qu.: 3.223 | 1st Qu.: 2.4489 | 1st Qu.: 5.98 | 1st Qu.: 9.488 | 1st Qu.:1.0000 | 1st Qu.: 87509 | 1st Qu.:1.981e+09 | 1st Qu.:1.221e+13 | 1st Qu.:2.353e+10 | 1st Qu.:0.2974 | 1st Qu.:0.0255 | 1st Qu.:0.0006 | 1st Qu.: 0.0016 | 1st Qu.:6.300e+06 | 1st Qu.:6.696e+12 | 1st Qu.:1.390e+13 | 1st Qu.:1.204e+10 | 1st Qu.:2.229e+09 | 1st Qu.:2.418e+11 | 1st Qu.: 0.1256 | 1st Qu.: 0.0030 | 1st Qu.: 0.0053 | 1st Qu.: 0.0008 | 1st Qu.: 0.0001 | 1st Qu.: 0.0154 | 1st Qu.:0.0007 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0 | 1st Qu.: 1721 | 1st Qu.:0.0625 | 1st Qu.:0.1858 | 1st Qu.:0.2851 | 1st Qu.: 5.152 | 1st Qu.: 3.424 | 1st Qu.: 2.6169 | 1st Qu.: 60814 | 1st Qu.:9.935e+08 | 1st Qu.:4.544e+12 | 1st Qu.:8.991e+09 | 1st Qu.:0.3269 | 1st Qu.:0.0304 | 1st Qu.:0.0008 | 1st Qu.: 0.0017 | 1st Qu.:3.855e+06 | 1st Qu.:2.554e+12 | 1st Qu.:4.996e+12 | 1st Qu.:4.610e+09 | 1st Qu.:1.031e+09 | 1st Qu.:9.700e+10 | 1st Qu.: 0.1632 | 1st Qu.: 0.0050 | 1st Qu.: 0.009 | 1st Qu.: 0.0009 | 1st Qu.: 0.0002 | 1st Qu.: 0.0213 | 1st Qu.:0.0011 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.:0.0000 | 1st Qu.: 1345.6 | 1st Qu.:0.0602 | 1st Qu.:0.1798 | 1st Qu.:0.2815 | 1st Qu.: 4.652 | 1st Qu.: 3.168 | 1st Qu.: 2.4482 | 1st Qu.: 0.0 | 1st Qu.: 0.00 | 1st Qu.:0.0000 | 1st Qu.: 80446 | 1st Qu.:1.677e+09 | 1st Qu.:9.703e+12 | 1st Qu.:1.576e+10 | 1st Qu.:0.286 | 1st Qu.:0.024 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:5.225e+06 | 1st Qu.:4.915e+12 | 1st Qu.:9.243e+12 | 1st Qu.:7.840e+09 | 1st Qu.:1.139e+09 | 1st Qu.:1.751e+11 | 1st Qu.: 0.113 | 1st Qu.: 0.003 | 1st Qu.: 0.004 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.013 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1641 | 1st Qu.:0.058 | 1st Qu.:0.178 | 1st Qu.:0.276 | 1st Qu.: 4.844 | 1st Qu.: 3.364 | 1st Qu.: 2.599 | 1st Qu.: 60306 | 1st Qu.:9.973e+08 | 1st Qu.:4.631e+12 | 1st Qu.:6.872e+09 | 1st Qu.: 0.297 | 1st Qu.: 0.025 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.494e+06 | 1st Qu.:2.266e+12 | 1st Qu.:3.813e+12 | 1st Qu.:3.509e+09 | 1st Qu.:5.793e+08 | 1st Qu.:8.407e+10 | 1st Qu.: 0.131 | 1st Qu.: 0.003 | 1st Qu.: 0.01 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.016 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1421 | 1st Qu.:0.057 | 1st Qu.:0.173 | 1st Qu.:0.273 | 1st Qu.: 4.382 | 1st Qu.: 3.117 | 1st Qu.: 2.435 | 1st Qu.: 0.000 | 1st Qu.: 0.0 | 1st Qu.:0.0000 | 1st Qu.: 71079 | 1st Qu.:1.326e+09 | 1st Qu.:6.878e+12 | 1st Qu.:1.046e+10 | 1st Qu.:0.279 | 1st Qu.:0.024 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:4.357e+06 | 1st Qu.:3.416e+12 | 1st Qu.:6.267e+12 | 1st Qu.:4.954e+09 | 1st Qu.:6.858e+08 | 1st Qu.:1.308e+11 | 1st Qu.: 0.105 | 1st Qu.: 0.002 | 1st Qu.: 0.003 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.011 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1532 | 1st Qu.:0.055 | 1st Qu.:0.174 | 1st Qu.:0.267 | 1st Qu.: 4.606 | 1st Qu.: 3.294 | 1st Qu.: 2.562 | 1st Qu.: 59308 | 1st Qu.:9.687e+08 | 1st Qu.:4.582e+12 | 1st Qu.:5.444e+09 | 1st Qu.: 0.275 | 1st Qu.: 0.022 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.176e+06 | 1st Qu.:1.856e+12 | 1st Qu.:2.981e+12 | 1st Qu.:2.664e+09 | 1st Qu.:3.907e+08 | 1st Qu.:7.490e+10 | 1st Qu.: 0.111 | 1st Qu.: 0.002 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.012 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1439.6 | 1st Qu.:0.054 | 1st Qu.:0.168 | 1st Qu.:0.265 | 1st Qu.: 4.201 | 1st Qu.: 3.066 | 1st Qu.: 2.413 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.00 | 1st Qu.: 63638 | 1st Qu.:1.119e+09 | 1st Qu.:5.454e+12 | 1st Qu.:7.214e+09 | 1st Qu.:0.273 | 1st Qu.:0.023 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:3.620e+06 | 1st Qu.:2.433e+12 | 1st Qu.:4.051e+12 | 1st Qu.:3.319e+09 | 1st Qu.:3.608e+08 | 1st Qu.:9.492e+10 | 1st Qu.: 0.099 | 1st Qu.: 0.002 | 1st Qu.: 0.003 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.010 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1461 | 1st Qu.:0.054 | 1st Qu.:0.169 | 1st Qu.:0.273 | 1st Qu.: 4.408 | 1st Qu.: 3.226 | 1st Qu.: 2.530 | 1st Qu.: 58067 | 1st Qu.:9.551e+08 | 1st Qu.:4.472e+12 | 1st Qu.:4.431e+09 | 1st Qu.: 0.253 | 1st Qu.:0.019 | 1st Qu.:0.000 | 1st Qu.: 0.001 | 1st Qu.:2.854e+06 | 1st Qu.:1.644e+12 | 1st Qu.:2.343e+12 | 1st Qu.:2.080e+09 | 1st Qu.:2.396e+08 | 1st Qu.:6.389e+10 | 1st Qu.: 0.094 | 1st Qu.: 0.002 | 1st Qu.: 0.0 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.009 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1473.5 | 1st Qu.:0.053 | 1st Qu.:0.163 | 1st Qu.:0.271 | 1st Qu.: 4.032 | 1st Qu.: 3.000 | 1st Qu.: 2.393 | 1st Qu.: 0.00 | 1st Qu.: 0.00 | 1st Qu.:0.0000 | 1st Qu.: 57285 | 1st Qu.:9.262e+08 | 1st Qu.:4.274e+12 | 1st Qu.:4.645e+09 | 1st Qu.: 0.266 | 1st Qu.:0.022 | 1st Qu.:0.001 | 1st Qu.: 0.001 | 1st Qu.:2.981e+06 | 1st Qu.:1.764e+12 | 1st Qu.:2.516e+12 | 1st Qu.:2.130e+09 | 1st Qu.:2.076e+08 | 1st Qu.:6.809e+10 | 1st Qu.: 0.093 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.009 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1401 | 1st Qu.:0.056 | 1st Qu.:0.166 | 1st Qu.:0.287 | 1st Qu.: 4.190 | 1st Qu.: 3.164 | 1st Qu.: 2.517 | 1st Qu.: 56014 | 1st Qu.:9.081e+08 | 1st Qu.:4.089e+12 | 1st Qu.:3.213e+09 | 1st Qu.: 0.234 | 1st Qu.:0.017 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.507e+06 | 1st Qu.:1.371e+12 | 1st Qu.:1.719e+12 | 1st Qu.:1.521e+09 | 1st Qu.:1.409e+08 | 1st Qu.:5.238e+10 | 1st Qu.: 0.080 | 1st Qu.: 0.001 | 1st Qu.: 0.00 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0.000 | 1st Qu.: 1485.3 | 1st Qu.:0.056 | 1st Qu.:0.162 | 1st Qu.:0.288 | 1st Qu.: 3.841 | 1st Qu.: 2.954 | 1st Qu.: 2.384 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 51008 | 1st Qu.:7.495e+08 | 1st Qu.:3.256e+12 | 1st Qu.:2.976e+09 | 1st Qu.:0.259 | 1st Qu.:0.021 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:2.419e+06 | 1st Qu.:1.272e+12 | 1st Qu.:1.559e+12 | 1st Qu.:1.351e+09 | 1st Qu.:1.110e+08 | 1st Qu.:4.633e+10 | 1st Qu.: 0.087 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.008 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1348 | 1st Qu.:0.061 | 1st Qu.:0.171 | 1st Qu.:0.321 | 1st Qu.: 3.990 | 1st Qu.: 3.089 | 1st Qu.: 2.522 | 1st Qu.: 53459 | 1st Qu.:8.456e+08 | 1st Qu.:3.990e+12 | 1st Qu.:2.255e+09 | 1st Qu.:0.220 | 1st Qu.:0.015 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.225e+06 | 1st Qu.:1.118e+12 | 1st Qu.:1.272e+12 | 1st Qu.:1.056e+09 | 1st Qu.:8.450e+07 | 1st Qu.:4.341e+10 | 1st Qu.: 0.069 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.006 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1511.6 | 1st Qu.:0.061 | 1st Qu.:0.165 | 1st Qu.:0.318 | 1st Qu.: 3.670 | 1st Qu.: 2.887 | 1st Qu.: 2.384 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 46145 | 1st Qu.:6.309e+08 | 1st Qu.:2.522e+12 | 1st Qu.:1.987e+09 | 1st Qu.:0.255 | 1st Qu.:0.021 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:2.023e+06 | 1st Qu.:9.101e+11 | 1st Qu.:1.081e+12 | 1st Qu.:8.812e+08 | 1st Qu.:6.662e+07 | 1st Qu.:3.486e+10 | 1st Qu.: 0.082 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0 | 1st Qu.: 1298 | 1st Qu.:0.071 | 1st Qu.:0.170 | 1st Qu.:0.371 | 1st Qu.: 3.840 | 1st Qu.: 3.024 | 1st Qu.: 2.518 | 1st Qu.: 52102 | 1st Qu.:8.038e+08 | 1st Qu.:3.678e+12 | 1st Qu.:1.671e+09 | 1st Qu.:0.211 | 1st Qu.:0.013 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:2.021e+06 | 1st Qu.:9.610e+11 | 1st Qu.:1.025e+12 | 1st Qu.:7.524e+08 | 1st Qu.:5.455e+07 | 1st Qu.:3.837e+10 | 1st Qu.: 0.062 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.005 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0 | 1st Qu.: 1531.5 | 1st Qu.:0.071 | 1st Qu.:0.162 | 1st Qu.:0.369 | 1st Qu.: 3.547 | 1st Qu.: 2.827 | 1st Qu.: 2.378 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.:0.0000 | 1st Qu.: 43744 | 1st Qu.:5.732e+08 | 1st Qu.:2.284e+12 | 1st Qu.:1.446e+09 | 1st Qu.:0.251 | 1st Qu.:0.020 | 1st Qu.:0.001 | 1st Qu.: 0.000 | 1st Qu.:1.885e+06 | 1st Qu.:8.044e+11 | 1st Qu.:8.956e+11 | 1st Qu.:6.424e+08 | 1st Qu.:4.822e+07 | 1st Qu.:3.039e+10 | 1st Qu.: 0.080 | 1st Qu.: 0.002 | 1st Qu.: 0.002 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.007 | 1st Qu.:0.001 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.00 | 1st Qu.:0 | 1st Qu.: 1283 | 1st Qu.:0.083 | 1st Qu.:0.164 | 1st Qu.:0.415 | 1st Qu.: 3.771 | 1st Qu.: 2.979 | 1st Qu.: 2.513 | 1st Qu.: 50807 | 1st Qu.:7.782e+08 | 1st Qu.:3.589e+12 | 1st Qu.:1.327e+09 | 1st Qu.: 0.205 | 1st Qu.: 0.013 | 1st Qu.:0.000 | 1st Qu.: 0.000 | 1st Qu.:1.936e+06 | 1st Qu.:8.855e+11 | 1st Qu.:9.161e+11 | 1st Qu.:6.017e+08 | 1st Qu.:4.189e+07 | 1st Qu.:3.637e+10 | 1st Qu.: 0.058 | 1st Qu.: 0.001 | 1st Qu.: 0.001 | 1st Qu.: 0.000 | 1st Qu.: 0.000 | 1st Qu.: 0.004 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.:0.000 | 1st Qu.: 1565.5 | 1st Qu.:0.082 | 1st Qu.:0.156 | 1st Qu.:0.416 | 1st Qu.: 3.502 | 1st Qu.: 2.793 | 1st Qu.: 2.379 | 1st Qu.: 222.94 | 1st Qu.: 9.469 | 1st Qu.:0.02630 | 1st Qu.:0.1127 | 1st Qu.:0 | 1st Qu.: 0.819 | 1st Qu.:0.1811 | 1st Qu.:1.0000 | NA | NA | NA’s:14132 | 1st Qu.:0.2 | 1st Qu.:1.9 | 1st Qu.:1.4 | 1st Qu.:2 | 1st Qu.:1.800 | 1st Qu.:-7.560e-11 | 1st Qu.:0.209427 | 1st Qu.:0.1013822 | 1st Qu.:-1.1336 | 1st Qu.: 1.7394 | 1st Qu.:-6.600e-11 | 1st Qu.:0.20189 | 1st Qu.:0.103773 | 1st Qu.:-1.0312 | 1st Qu.: 1.6707 | 1st Qu.:-1.104e-10 | 1st Qu.:0.099798 | 1st Qu.:0.0220144 | 1st Qu.: -0.89931 | 1st Qu.: 1.29550 | 1st Qu.:-3.450e-11 | 1st Qu.:0.188691 | 1st Qu.:0.094683 | 1st Qu.:-0.9041 | 1st Qu.: 1.5904 | 1st Qu.: 303069 | 1st Qu.: 316088 | 1st Qu.: 522748 | 1st Qu.:0.2312 | 1st Qu.:-0.00873 | 1st Qu.:0.08642 | 1st Qu.:-0.1686900 | 1st Qu.:0.16439 | 1st Qu.:7.875e-02 | 1st Qu.:0.28159 | 1st Qu.:-0.00762 | 1st Qu.:0.05653 | 1st Qu.:-0.15855 | 1st Qu.:0.14448 | 1st Qu.: 0.073746 | 1st Qu.:0.28148 | 1st Qu.:-0.00317 | 1st Qu.:0.08416 | 1st Qu.:-0.028594 | 1st Qu.:0.10356 | 1st Qu.: 0.00600 | 1st Qu.:0.11254 | 1st Qu.:-0.00675 | 1st Qu.:0.03232 | 1st Qu.:-0.143829 | 1st Qu.:0.12733 | 1st Qu.: 0.066065 | 1st Qu.:0.26695 | 1st Qu.:-0.33681 | 1st Qu.:0.12728 | 1st Qu.:0.000e+00 | 1st Qu.:0.000e+00 | 1st Qu.: 0.3844 | 1st Qu.:-0.00788 | 1st Qu.:0.08475 | 1st Qu.:2.5 | 1st Qu.:7.1 | 1st Qu.:0.5 | |
| 115l BME 901 A: 1 | 3abk : 14 | GOL : 778 | 501 : 417 | C : 795 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Median : 7.00 | Median : 7.00 | Median : 7.00 | Median : 55.0 | Median : 52.00 | Median : 3.000 | Median : 0.00 | Median : 3.000 | Median :0.0000 | Median : 7.00 | Median : 56.0 | Median : 3.00 | Median : 0.000 | Median : 3.000 | Median :0.0000 | Median : 18.614 | Median : 27.30 | Median :1.0000 | Median : 392324 | Median :3.374e+10 | Median :7.154e+14 | Median :1.747e+12 | Median :0.4826 | Median :0.0508 | Median :0.0012 | Median :0.0129 | Median :6.683e+07 | Median :5.855e+14 | Median :2.106e+15 | Median :1.004e+12 | Median :3.677e+11 | Median :1.259e+13 | Median : 0.3602 | Median : 0.0167 | Median : 0.0633 | Median :0.0074 | Median :0.0026 | Median : 0.0873 | Median :0.0014 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0e+00 | Median :0 | Median : 3580 | Median :0.1328 | Median :0.3432 | Median :0.5061 | Median : 8.634 | Median : 4.749 | Median : 3.275 | Median : 201961 | Median :8.933e+09 | Median :1.036e+14 | Median :4.992e+11 | Median :0.6608 | Median :0.0950 | Median :0.0030 | Median : 0.0331 | Median :3.292e+07 | Median :1.356e+14 | Median :5.501e+14 | Median :3.324e+11 | Median :1.688e+11 | Median :3.315e+12 | Median : 0.7094 | Median : 0.0617 | Median : 0.2383 | Median : 0.0212 | Median : 0.0092 | Median : 0.2346 | Median :0.0035 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2421 | Median :0.1277 | Median :0.3387 | Median :0.5086 | Median : 8.278 | Median : 4.407 | Median : 3.0316 | Median : 17.561 | Median : 24.51 | Median :1.0000 | Median : 371987 | Median :3.003e+10 | Median :5.893e+14 | Median :1.510e+12 | Median :0.4898 | Median :0.0516 | Median :0.0012 | Median :0.0133 | Median :6.310e+07 | Median :4.979e+14 | Median :1.913e+15 | Median :8.845e+11 | Median :3.127e+11 | Median :1.150e+13 | Median : 0.3698 | Median : 0.0172 | Median : 0.0669 | Median :0.0077 | Median :0.0028 | Median : 0.0905 | Median :0.0014 | Median :0.0000 | Median :0.000 | Median :0.0000 | Median :0.000 | Median :0 | Median : 3438 | Median :0.1310 | Median :0.3406 | Median :0.5083 | Median : 8.615 | Median : 4.687 | Median : 3.2292 | Median : 202072 | Median :8.784e+09 | Median :9.831e+13 | Median :4.894e+11 | Median :0.6567 | Median :0.0935 | Median :0.0029 | Median : 0.0320 | Median :3.248e+07 | Median :1.308e+14 | Median :5.311e+14 | Median :3.206e+11 | Median :1.575e+11 | Median :3.324e+12 | Median : 0.6992 | Median : 0.0591 | Median : 0.234 | Median : 0.0207 | Median : 0.0089 | Median : 0.2290 | Median :0.0035 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2417.00 | Median :0.1259 | Median :0.3374 | Median :0.5097 | Median : 8.306 | Median : 4.348 | Median : 2.9975 | Median : 14.92 | Median : 18.472 | Median :1.0000 | Median : 353222 | Median :2.632e+10 | Median :4.586e+14 | Median :1.271e+12 | Median :0.5084 | Median :0.0541 | Median :0.0013 | Median : 0.0147 | Median :5.957e+07 | Median :4.354e+14 | Median :1.765e+15 | Median :7.309e+11 | Median :2.667e+11 | Median :1.056e+13 | Median : 0.3991 | Median : 0.0192 | Median : 0.0794 | Median : 0.0086 | Median : 0.0031 | Median : 0.1038 | Median :0.0016 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0 | Median : 3199 | Median :0.1240 | Median :0.3398 | Median :0.5108 | Median : 8.686 | Median : 4.593 | Median : 3.1510 | Median : 217227 | Median :9.850e+09 | Median :1.054e+14 | Median :5.136e+11 | Median :0.6472 | Median :0.0889 | Median :0.0027 | Median : 0.0303 | Median :3.677e+07 | Median :1.554e+14 | Median :7.150e+14 | Median :3.379e+11 | Median :1.608e+11 | Median :4.074e+12 | Median : 0.6657 | Median : 0.0525 | Median : 0.219 | Median : 0.0196 | Median : 0.0080 | Median : 0.2166 | Median :0.0034 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median :0.0000 | Median : 2471.8 | Median :0.1191 | Median :0.3397 | Median :0.5135 | Median : 8.412 | Median : 4.310 | Median : 2.9366 | Median : 12.2 | Median : 13.93 | Median :1.0000 | Median : 332182 | Median :2.274e+10 | Median :3.814e+14 | Median :1.155e+12 | Median :0.523 | Median :0.056 | Median :0.001 | Median : 0.016 | Median :5.614e+07 | Median :3.576e+14 | Median :1.544e+15 | Median :6.599e+11 | Median :2.269e+11 | Median :9.433e+12 | Median : 0.420 | Median : 0.021 | Median : 0.089 | Median : 0.009 | Median : 0.003 | Median : 0.114 | Median :0.002 | Median :0.000 | Median :0.00 | Median :0.000 | Median :0.000 | Median :0 | Median : 2992 | Median :0.121 | Median :0.346 | Median :0.518 | Median : 8.678 | Median : 4.500 | Median : 3.088 | Median : 228546 | Median :1.021e+10 | Median :1.089e+14 | Median :5.777e+11 | Median : 0.628 | Median : 0.084 | Median :0.002 | Median : 0.028 | Median :3.763e+07 | Median :1.593e+14 | Median :7.299e+14 | Median :3.627e+11 | Median :1.751e+11 | Median :4.446e+12 | Median : 0.630 | Median : 0.047 | Median : 0.20 | Median : 0.018 | Median : 0.008 | Median : 0.200 | Median :0.003 | Median :0.000 | Median : 0.000 | Median :0.00 | Median :0.000 | Median :0.000 | Median : 2517 | Median :0.117 | Median :0.347 | Median :0.521 | Median : 8.473 | Median : 4.238 | Median : 2.885 | Median : 9.215 | Median : 10.6 | Median :1.0000 | Median : 280935 | Median :1.629e+10 | Median :2.224e+14 | Median :7.137e+11 | Median :0.525 | Median :0.056 | Median :0.001 | Median : 0.015 | Median :4.370e+07 | Median :2.246e+14 | Median :9.829e+14 | Median :4.405e+11 | Median :1.563e+11 | Median :6.147e+12 | Median : 0.429 | Median : 0.021 | Median : 0.091 | Median : 0.009 | Median : 0.003 | Median : 0.116 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2709 | Median :0.119 | Median :0.350 | Median :0.527 | Median : 8.460 | Median : 4.360 | Median : 3.012 | Median : 208895 | Median :8.384e+09 | Median :8.358e+13 | Median :4.365e+11 | Median : 0.594 | Median : 0.076 | Median :0.002 | Median : 0.024 | Median :3.302e+07 | Median :1.173e+14 | Median :6.077e+14 | Median :2.828e+11 | Median :1.362e+11 | Median :3.757e+12 | Median : 0.569 | Median : 0.040 | Median : 0.16 | Median : 0.015 | Median : 0.006 | Median : 0.172 | Median :0.003 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2479.6 | Median :0.114 | Median :0.348 | Median :0.533 | Median : 8.245 | Median : 4.104 | Median : 2.825 | Median : 3.495 | Median : 8.224 | Median :1.00 | Median : 235095 | Median :1.149e+10 | Median :1.206e+14 | Median :4.385e+11 | Median :0.516 | Median :0.053 | Median :0.001 | Median : 0.014 | Median :3.362e+07 | Median :1.342e+14 | Median :5.714e+14 | Median :2.675e+11 | Median :9.867e+10 | Median :4.056e+12 | Median : 0.406 | Median : 0.019 | Median : 0.081 | Median : 0.008 | Median : 0.003 | Median : 0.108 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2462 | Median :0.121 | Median :0.350 | Median :0.548 | Median : 8.105 | Median : 4.177 | Median : 2.942 | Median : 191922 | Median :7.194e+09 | Median :6.487e+13 | Median :3.234e+11 | Median : 0.545 | Median :0.066 | Median :0.002 | Median : 0.017 | Median :2.862e+07 | Median :8.374e+13 | Median :4.300e+14 | Median :2.165e+11 | Median :1.037e+11 | Median :3.022e+12 | Median : 0.481 | Median : 0.030 | Median : 0.1 | Median : 0.011 | Median : 0.005 | Median : 0.129 | Median :0.003 | Median :0.000 | Median : 0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2418.0 | Median :0.117 | Median :0.351 | Median :0.556 | Median : 7.926 | Median : 3.941 | Median : 2.760 | Median : 0.00 | Median : 0.00 | Median :0.0000 | Median : 185677 | Median :7.099e+09 | Median :6.726e+13 | Median :2.095e+11 | Median : 0.485 | Median :0.048 | Median :0.001 | Median : 0.010 | Median :2.342e+07 | Median :6.457e+13 | Median :2.739e+14 | Median :1.217e+11 | Median :4.539e+10 | Median :2.319e+12 | Median : 0.353 | Median : 0.014 | Median : 0.061 | Median : 0.006 | Median : 0.002 | Median : 0.090 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0.000 | Median :0 | Median : 2232 | Median :0.129 | Median :0.370 | Median :0.585 | Median : 7.658 | Median : 3.950 | Median : 2.899 | Median : 165427 | Median :5.518e+09 | Median :4.585e+13 | Median :1.829e+11 | Median : 0.475 | Median :0.051 | Median :0.001 | Median : 0.009 | Median :2.158e+07 | Median :4.817e+13 | Median :2.463e+14 | Median :1.245e+11 | Median :5.890e+10 | Median :1.962e+12 | Median : 0.357 | Median : 0.017 | Median : 0.06 | Median : 0.006 | Median : 0.003 | Median : 0.085 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0.000 | Median : 2389.9 | Median :0.126 | Median :0.372 | Median :0.594 | Median : 7.482 | Median : 3.700 | Median : 2.717 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 150813 | Median :4.832e+09 | Median :3.949e+13 | Median :8.898e+10 | Median :0.441 | Median :0.042 | Median :0.001 | Median : 0.006 | Median :1.700e+07 | Median :3.448e+13 | Median :1.447e+14 | Median :5.238e+10 | Median :2.114e+10 | Median :1.353e+12 | Median : 0.298 | Median : 0.010 | Median : 0.043 | Median : 0.003 | Median : 0.001 | Median : 0.067 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2070 | Median :0.145 | Median :0.394 | Median :0.637 | Median : 7.263 | Median : 3.706 | Median : 2.857 | Median : 144521 | Median :4.340e+09 | Median :3.527e+13 | Median :9.948e+10 | Median :0.414 | Median :0.040 | Median :0.001 | Median : 0.004 | Median :1.634e+07 | Median :2.840e+13 | Median :1.462e+14 | Median :6.826e+10 | Median :3.280e+10 | Median :1.242e+12 | Median : 0.255 | Median : 0.009 | Median : 0.029 | Median : 0.003 | Median : 0.001 | Median : 0.052 | Median :0.002 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 2363.1 | Median :0.140 | Median :0.400 | Median :0.654 | Median : 7.036 | Median : 3.471 | Median : 2.681 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 127679 | Median :3.534e+09 | Median :2.584e+13 | Median :4.158e+10 | Median :0.407 | Median :0.036 | Median :0.001 | Median : 0.003 | Median :1.275e+07 | Median :1.843e+13 | Median :8.248e+13 | Median :2.365e+10 | Median :9.899e+09 | Median :8.806e+11 | Median : 0.254 | Median : 0.008 | Median : 0.031 | Median : 0.002 | Median : 0.001 | Median : 0.052 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0 | Median : 1916 | Median :0.158 | Median :0.414 | Median :0.684 | Median : 6.899 | Median : 3.489 | Median : 2.817 | Median : 127405 | Median :3.506e+09 | Median :2.632e+13 | Median :5.500e+10 | Median :0.371 | Median :0.030 | Median :0.001 | Median : 0.002 | Median :1.319e+07 | Median :1.688e+13 | Median :9.255e+13 | Median :3.616e+10 | Median :1.775e+10 | Median :8.919e+11 | Median : 0.198 | Median : 0.005 | Median : 0.017 | Median : 0.001 | Median : 0.001 | Median : 0.037 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0 | Median : 2328.1 | Median :0.156 | Median :0.421 | Median :0.700 | Median : 6.616 | Median : 3.275 | Median : 2.650 | Median : 0.000 | Median : 0.000 | Median :0.0000 | Median : 116713 | Median :2.822e+09 | Median :1.990e+13 | Median :2.428e+10 | Median :0.392 | Median :0.034 | Median :0.001 | Median : 0.002 | Median :1.118e+07 | Median :1.360e+13 | Median :6.380e+13 | Median :1.480e+10 | Median :5.684e+09 | Median :7.004e+11 | Median : 0.234 | Median : 0.006 | Median : 0.027 | Median : 0.001 | Median : 0.000 | Median : 0.046 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.00 | Median :0 | Median : 1847 | Median :0.167 | Median :0.413 | Median :0.722 | Median : 6.706 | Median : 3.360 | Median : 2.773 | Median : 124612 | Median :3.209e+09 | Median :2.297e+13 | Median :3.406e+10 | Median : 0.347 | Median : 0.026 | Median :0.001 | Median : 0.001 | Median :1.204e+07 | Median :1.361e+13 | Median :7.942e+13 | Median :2.196e+10 | Median :1.126e+10 | Median :7.923e+11 | Median : 0.175 | Median : 0.004 | Median : 0.014 | Median : 0.001 | Median : 0.000 | Median : 0.030 | Median :0.001 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median :0.000 | Median : 2294.0 | Median :0.166 | Median :0.422 | Median :0.734 | Median : 6.407 | Median : 3.166 | Median : 2.618 | Median : 414.56 | Median : 18.614 | Median :0.03704 | Median :0.1532 | Median :0 | Median : 1.281 | Median :0.2529 | Median :1.0000 | NA | NA | NA | Median :0.2 | Median :1.9 | Median :1.4 | Median :2 | Median :2.040 | Median : 1.046e-10 | Median :0.273372 | Median :0.1720145 | Median :-0.9347 | Median : 2.5344 | Median : 1.430e-10 | Median :0.26292 | Median :0.177083 | Median :-0.8379 | Median : 2.4168 | Median : 6.200e-12 | Median :0.130447 | Median :0.0413683 | Median : -0.71010 | Median : 1.93808 | Median : 1.402e-10 | Median :0.245466 | Median :0.163666 | Median :-0.7254 | Median : 2.3201 | Median : 717445 | Median : 514708 | Median : 885746 | Median :0.3320 | Median :-0.00457 | Median :0.11254 | Median :-0.1453470 | Median :0.19674 | Median :8.654e-02 | Median :0.35584 | Median :-0.00383 | Median :0.07331 | Median :-0.13823 | Median :0.17009 | Median : 0.082145 | Median :0.35579 | Median :-0.00132 | Median :0.10912 | Median :-0.018357 | Median :0.13054 | Median : 0.01149 | Median :0.14498 | Median :-0.00305 | Median :0.04169 | Median :-0.126363 | Median :0.15054 | Median : 0.074527 | Median :0.33948 | Median :-0.30237 | Median :0.14707 | Median :0.000e+00 | Median :0.000e+00 | Median : 0.4401 | Median :-0.00367 | Median :0.11066 | Median :2.5 | Median :7.1 | Median :0.5 | |
| 115l CL 173 A : 1 | 2afh : 11 | CA : 661 | 401 : 395 | D : 552 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Mean : 13.59 | Mean : 13.43 | Mean : 13.05 | Mean :101.5 | Mean : 98.33 | Mean : 7.332 | Mean : 1.35 | Mean : 3.817 | Mean :0.2208 | Mean : 13.74 | Mean :103.8 | Mean : 7.47 | Mean : 1.357 | Mean : 3.979 | Mean :0.2213 | Mean : 31.535 | Mean : 51.45 | Mean :0.9725 | Mean : 3091661 | Mean :1.622e+13 | Mean :1.938e+20 | Mean :2.123e+16 | Mean :0.5752 | Mean :0.0782 | Mean :0.0026 | Mean :0.0651 | Mean :4.023e+09 | Mean :1.143e+20 | Mean :6.415e+20 | Mean :1.253e+16 | Mean :6.720e+15 | Mean :1.592e+17 | Mean : 0.6958 | Mean : 0.1159 | Mean : 1.0010 | Mean :0.0416 | Mean :0.0260 | Mean : 0.4266 | Mean :0.0022 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0e+00 | Mean :0 | Mean : 6622 | Mean :0.2174 | Mean :0.4061 | Mean :0.5133 | Mean :10.851 | Mean : 5.807 | Mean : 3.682 | Mean : 1670086 | Mean :3.188e+12 | Mean :4.092e+18 | Mean :3.482e+15 | Mean :0.7665 | Mean :0.1466 | Mean :0.0071 | Mean : 0.2521 | Mean :2.036e+09 | Mean :1.025e+19 | Mean :7.811e+19 | Mean :2.193e+15 | Mean :1.334e+15 | Mean :2.463e+16 | Mean : 1.4490 | Mean : 0.6412 | Mean : 6.6229 | Mean : 0.1849 | Mean : 0.1401 | Mean : 1.4628 | Mean :0.0078 | Mean :0.0000 | Mean :0.0004 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 4059 | Mean :0.2206 | Mean :0.4058 | Mean :0.5151 | Mean :10.400 | Mean : 5.487 | Mean : 3.4481 | Mean : 29.751 | Mean : 46.67 | Mean :0.9522 | Mean : 2894498 | Mean :1.384e+13 | Mean :1.558e+20 | Mean :1.829e+16 | Mean :0.5905 | Mean :0.0823 | Mean :0.0028 | Mean :0.0756 | Mean :3.739e+09 | Mean :9.661e+19 | Mean :5.336e+20 | Mean :1.094e+16 | Mean :6.034e+15 | Mean :1.347e+17 | Mean : 0.7492 | Mean : 0.1351 | Mean : 1.2288 | Mean :0.0491 | Mean :0.0315 | Mean : 0.4913 | Mean :0.0024 | Mean :0.0000 | Mean :0.000 | Mean :0.0000 | Mean :0.000 | Mean :0 | Mean : 6285 | Mean :0.2203 | Mean :0.4073 | Mean :0.5139 | Mean :10.825 | Mean : 5.750 | Mean : 3.6288 | Mean : 1627744 | Mean :2.936e+12 | Mean :3.562e+18 | Mean :3.237e+15 | Mean :0.7698 | Mean :0.1473 | Mean :0.0071 | Mean : 0.2734 | Mean :1.973e+09 | Mean :9.264e+18 | Mean :7.123e+19 | Mean :2.063e+15 | Mean :1.281e+15 | Mean :2.274e+16 | Mean : 1.4893 | Mean : 0.6688 | Mean : 7.645 | Mean : 0.2013 | Mean : 0.1533 | Mean : 1.5884 | Mean :0.0079 | Mean :0.0000 | Mean :0.0004 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 4006.64 | Mean :0.2236 | Mean :0.4073 | Mean :0.5159 | Mean :10.396 | Mean : 5.447 | Mean : 3.4090 | Mean : 25.29 | Mean : 36.283 | Mean :0.8789 | Mean : 2498087 | Mean :9.441e+12 | Mean :9.121e+19 | Mean :1.266e+16 | Mean :0.6297 | Mean :0.0941 | Mean :0.0033 | Mean : 0.1094 | Mean :3.159e+09 | Mean :6.363e+19 | Mean :3.286e+20 | Mean :7.812e+15 | Mean :4.583e+15 | Mean :8.826e+16 | Mean : 0.8958 | Mean : 0.2128 | Mean : 2.0358 | Mean : 0.0736 | Mean : 0.0498 | Mean : 0.6871 | Mean :0.0030 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0.0000 | Mean :0 | Mean : 5623 | Mean :0.2274 | Mean :0.4111 | Mean :0.5145 | Mean :10.830 | Mean : 5.664 | Mean : 3.5406 | Mean : 1535388 | Mean :2.373e+12 | Mean :2.490e+18 | Mean :2.667e+15 | Mean :0.7767 | Mean :0.1502 | Mean :0.0071 | Mean : 0.3481 | Mean :1.830e+09 | Mean :7.042e+18 | Mean :5.556e+19 | Mean :1.749e+15 | Mean :1.137e+15 | Mean :1.846e+16 | Mean : 1.5867 | Mean : 0.8918 | Mean : 10.868 | Mean : 0.2686 | Mean : 0.2157 | Mean : 1.8945 | Mean :0.0081 | Mean :0.0000 | Mean :0.0005 | Mean :0.0001 | Mean :0.0001 | Mean :0.0000 | Mean : 3919.5 | Mean :0.2311 | Mean :0.4116 | Mean :0.5172 | Mean :10.432 | Mean : 5.387 | Mean : 3.3448 | Mean : 21.1 | Mean : 28.00 | Mean :0.7844 | Mean : 2143093 | Mean :6.474e+12 | Mean :5.200e+19 | Mean :9.143e+15 | Mean :0.664 | Mean :0.105 | Mean :0.004 | Mean : 0.147 | Mean :2.643e+09 | Mean :4.120e+19 | Mean :2.049e+20 | Mean :5.802e+15 | Mean :3.575e+15 | Mean :5.861e+16 | Mean : 1.043 | Mean : 0.320 | Mean : 3.088 | Mean : 0.100 | Mean : 0.068 | Mean : 0.928 | Mean :0.004 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 5042 | Mean :0.236 | Mean :0.417 | Mean :0.518 | Mean :10.758 | Mean : 5.557 | Mean : 3.458 | Mean : 1416666 | Mean :1.856e+12 | Mean :1.666e+18 | Mean :2.191e+15 | Mean : 0.780 | Mean : 0.153 | Mean :0.007 | Mean : 0.434 | Mean :1.652e+09 | Mean :5.106e+18 | Mean :4.232e+19 | Mean :1.487e+15 | Mean :1.019e+15 | Mean :1.456e+16 | Mean : 1.669 | Mean : 1.234 | Mean : 19.76 | Mean : 0.334 | Mean : 0.268 | Mean : 2.430 | Mean :0.009 | Mean :0.000 | Mean : 0.002 | Mean :0.00 | Mean :0.000 | Mean :0.000 | Mean : 3800 | Mean :0.240 | Mean :0.418 | Mean :0.522 | Mean :10.377 | Mean : 5.298 | Mean : 3.279 | Mean : 17.299 | Mean : 21.4 | Mean :0.6916 | Mean : 1793267 | Mean :4.402e+12 | Mean :2.929e+19 | Mean :6.519e+15 | Mean :0.689 | Mean :0.113 | Mean :0.004 | Mean : 0.191 | Mean :2.154e+09 | Mean :2.754e+19 | Mean :1.289e+20 | Mean :4.253e+15 | Mean :2.743e+15 | Mean :3.912e+16 | Mean : 1.176 | Mean : 0.461 | Mean : 5.020 | Mean : 0.136 | Mean : 0.099 | Mean : 1.241 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 4477 | Mean :0.242 | Mean :0.422 | Mean :0.522 | Mean :10.534 | Mean : 5.386 | Mean : 3.353 | Mean : 1256622 | Mean :1.367e+12 | Mean :1.039e+18 | Mean :1.695e+15 | Mean : 0.771 | Mean : 0.148 | Mean :0.007 | Mean : 0.541 | Mean :1.425e+09 | Mean :3.628e+18 | Mean :3.046e+19 | Mean :1.206e+15 | Mean :8.804e+14 | Mean :1.084e+16 | Mean : 1.688 | Mean : 1.227 | Mean : 25.74 | Mean : 0.456 | Mean : 0.400 | Mean : 2.607 | Mean :0.008 | Mean :0.000 | Mean :0.001 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean : 3618.9 | Mean :0.246 | Mean :0.423 | Mean :0.526 | Mean :10.167 | Mean : 5.141 | Mean : 3.188 | Mean : 13.862 | Mean : 16.100 | Mean :0.59 | Mean : 1489327 | Mean :2.966e+12 | Mean :1.528e+19 | Mean :4.724e+15 | Mean :0.699 | Mean :0.116 | Mean :0.004 | Mean : 0.253 | Mean :1.726e+09 | Mean :1.838e+19 | Mean :8.501e+19 | Mean :3.188e+15 | Mean :2.164e+15 | Mean :2.655e+16 | Mean : 1.268 | Mean : 0.495 | Mean : 7.081 | Mean : 0.193 | Mean : 0.153 | Mean : 1.495 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 4002 | Mean :0.251 | Mean :0.427 | Mean :0.532 | Mean :10.218 | Mean : 5.179 | Mean : 3.254 | Mean : 1092621 | Mean :9.870e+11 | Mean :6.227e+17 | Mean :1.339e+15 | Mean : 0.753 | Mean :0.141 | Mean :0.006 | Mean : 1.045 | Mean :1.196e+09 | Mean :2.495e+18 | Mean :2.148e+19 | Mean :9.911e+14 | Mean :7.593e+14 | Mean :7.930e+15 | Mean : 1.737 | Mean : 0.956 | Mean : 73.8 | Mean : 0.958 | Mean : 0.900 | Mean : 3.705 | Mean :0.009 | Mean :0.000 | Mean : 0.009 | Mean :0.001 | Mean :0.001 | Mean :0.000 | Mean : 3446.0 | Mean :0.256 | Mean :0.428 | Mean :0.537 | Mean : 9.854 | Mean : 4.944 | Mean : 3.097 | Mean : 10.88 | Mean : 11.94 | Mean :0.4852 | Mean : 1222842 | Mean :2.019e+12 | Mean :8.302e+18 | Mean :3.378e+15 | Mean : 0.687 | Mean :0.112 | Mean :0.004 | Mean : 0.278 | Mean :1.350e+09 | Mean :1.269e+19 | Mean :5.687e+19 | Mean :2.295e+15 | Mean :1.573e+15 | Mean :1.828e+16 | Mean : 1.299 | Mean : 1.250 | Mean : 12.227 | Mean : 0.224 | Mean : 0.187 | Mean : 1.879 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean :0 | Mean : 3606 | Mean :0.266 | Mean :0.437 | Mean :0.550 | Mean : 9.751 | Mean : 4.941 | Mean : 3.170 | Mean : 926558 | Mean :6.708e+11 | Mean :3.182e+17 | Mean :9.803e+14 | Mean : 0.717 | Mean :0.129 | Mean :0.005 | Mean : 0.478 | Mean :9.550e+08 | Mean :1.601e+18 | Mean :1.355e+19 | Mean :7.529e+14 | Mean :6.013e+14 | Mean :5.339e+15 | Mean : 1.628 | Mean : 3.869 | Mean : 31.36 | Mean : 0.415 | Mean : 0.372 | Mean : 2.996 | Mean :0.008 | Mean :0.000 | Mean :0.002 | Mean :0.000 | Mean :0.00 | Mean :0.000 | Mean : 3285.4 | Mean :0.271 | Mean :0.439 | Mean :0.555 | Mean : 9.395 | Mean : 4.716 | Mean : 3.018 | Mean : 8.499 | Mean : 8.825 | Mean :0.3972 | Mean : 980603 | Mean :1.345e+12 | Mean :4.072e+18 | Mean :2.389e+15 | Mean :0.650 | Mean :0.103 | Mean :0.004 | Mean : 0.315 | Mean :1.037e+09 | Mean :8.459e+18 | Mean :3.897e+19 | Mean :1.649e+15 | Mean :1.156e+15 | Mean :1.262e+16 | Mean : 1.182 | Mean : 0.447 | Mean : 9.429 | Mean : 0.263 | Mean : 0.229 | Mean : 1.570 | Mean :0.005 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 3254 | Mean :0.285 | Mean :0.451 | Mean :0.577 | Mean : 9.133 | Mean : 4.684 | Mean : 3.096 | Mean : 761473 | Mean :4.467e+11 | Mean :1.706e+17 | Mean :6.972e+14 | Mean :0.659 | Mean :0.114 | Mean :0.005 | Mean : 0.406 | Mean :7.320e+08 | Mean :1.082e+18 | Mean :9.105e+18 | Mean :5.415e+14 | Mean :4.377e+14 | Mean :3.615e+15 | Mean : 1.407 | Mean : 0.689 | Mean : 11.904 | Mean : 0.343 | Mean : 0.300 | Mean : 2.001 | Mean :0.007 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 3133.6 | Mean :0.290 | Mean :0.453 | Mean :0.582 | Mean : 8.797 | Mean : 4.464 | Mean : 2.948 | Mean : 6.594 | Mean : 6.491 | Mean :0.3167 | Mean : 770168 | Mean :8.672e+11 | Mean :1.746e+18 | Mean :1.586e+15 | Mean :0.599 | Mean :0.088 | Mean :0.003 | Mean : 0.201 | Mean :7.658e+08 | Mean :5.362e+18 | Mean :2.466e+19 | Mean :1.111e+15 | Mean :7.948e+14 | Mean :8.283e+15 | Mean : 0.996 | Mean : 0.314 | Mean : 5.072 | Mean : 0.157 | Mean : 0.128 | Mean : 1.152 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0 | Mean : 2951 | Mean :0.302 | Mean :0.462 | Mean :0.604 | Mean : 8.469 | Mean : 4.391 | Mean : 3.013 | Mean : 607846 | Mean :2.916e+11 | Mean :8.687e+16 | Mean :4.451e+14 | Mean :0.596 | Mean :0.097 | Mean :0.004 | Mean : 0.321 | Mean :5.321e+08 | Mean :7.080e+17 | Mean :5.720e+18 | Mean :3.426e+14 | Mean :2.742e+14 | Mean :2.388e+15 | Mean : 1.205 | Mean : 0.627 | Mean : 8.923 | Mean : 0.262 | Mean : 0.223 | Mean : 1.636 | Mean :0.006 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0 | Mean : 2997.7 | Mean :0.306 | Mean :0.463 | Mean :0.610 | Mean : 8.159 | Mean : 4.179 | Mean : 2.866 | Mean : 5.125 | Mean : 4.805 | Mean :0.2491 | Mean : 615842 | Mean :5.264e+11 | Mean :5.428e+17 | Mean :1.288e+15 | Mean :0.564 | Mean :0.077 | Mean :0.003 | Mean : 0.197 | Mean :5.785e+08 | Mean :3.064e+18 | Mean :1.607e+19 | Mean :8.853e+14 | Mean :6.166e+14 | Mean :5.382e+15 | Mean : 0.908 | Mean : 0.327 | Mean : 6.447 | Mean : 0.167 | Mean : 0.147 | Mean : 1.216 | Mean :0.004 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean :0.00 | Mean :0 | Mean : 2720 | Mean :0.314 | Mean :0.464 | Mean :0.631 | Mean : 7.959 | Mean : 4.151 | Mean : 2.954 | Mean : 498566 | Mean :1.960e+11 | Mean :4.007e+16 | Mean :3.507e+14 | Mean : 0.550 | Mean : 0.085 | Mean :0.004 | Mean : 0.349 | Mean :4.013e+08 | Mean :4.416e+17 | Mean :4.001e+18 | Mean :2.741e+14 | Mean :2.230e+14 | Mean :1.661e+15 | Mean : 1.116 | Mean : 1.051 | Mean : 14.992 | Mean : 0.297 | Mean : 0.262 | Mean : 1.927 | Mean :0.006 | Mean :0.000 | Mean :0.001 | Mean :0.000 | Mean :0.000 | Mean :0.000 | Mean : 2900.9 | Mean :0.318 | Mean :0.465 | Mean :0.637 | Mean : 7.671 | Mean : 3.948 | Mean : 2.810 | Mean : 937.24 | Mean : 31.535 | Mean :0.04484 | Mean :0.1815 | Mean :0 | Mean : 1.690 | Mean :0.3039 | Mean :0.9725 | NA | NA | NA | Mean :0.2 | Mean :1.9 | Mean :1.4 | Mean :2 | Mean :2.128 | Mean :-7.000e-11 | Mean :0.275025 | Mean :0.1927164 | Mean :-0.9678 | Mean : 2.7854 | Mean : 2.942e-09 | Mean :0.26458 | Mean :0.197404 | Mean :-0.8724 | Mean : 2.5077 | Mean :-5.264e-10 | Mean :0.133948 | Mean :0.0575110 | Mean : -0.75040 | Mean : 2.38074 | Mean : 4.934e-10 | Mean :0.247163 | Mean :0.182585 | Mean :-0.7575 | Mean : 2.4045 | Mean : 1615555 | Mean : 745313 | Mean : 1369266 | Mean :0.3389 | Mean :-0.00593 | Mean :0.11494 | Mean :-0.1417209 | Mean :0.19472 | Mean :8.550e-02 | Mean :0.35322 | Mean :-0.00488 | Mean :0.07565 | Mean :-0.13486 | Mean :0.16813 | Mean : 0.081655 | Mean :0.35238 | Mean :-0.00190 | Mean :0.11144 | Mean :-0.018502 | Mean :0.13226 | Mean : 0.01071 | Mean :0.14801 | Mean :-0.00403 | Mean :0.04446 | Mean :-0.123219 | Mean :0.14792 | Mean : 0.074781 | Mean :0.33536 | Mean :-0.28451 | Mean :0.15577 | Mean :1.348e+73 | Mean :6.825e+71 | Mean : 0.4472 | Mean :-0.00524 | Mean :0.11325 | Mean :2.5 | Mean :7.1 | Mean :0.5 | |
| 123l BME 901 A: 1 | 3a0b : 11 | MG : 402 | 201 : 306 | E : 221 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3rd Qu.: 21.00 | 3rd Qu.: 20.00 | 3rd Qu.: 20.00 | 3rd Qu.:145.0 | 3rd Qu.:139.15 | 3rd Qu.:10.000 | 3rd Qu.: 2.00 | 3rd Qu.: 5.000 | 3rd Qu.:0.0000 | 3rd Qu.: 21.00 | 3rd Qu.:151.0 | 3rd Qu.:10.00 | 3rd Qu.: 2.000 | 3rd Qu.: 6.000 | 3rd Qu.:0.0000 | 3rd Qu.: 41.434 | 3rd Qu.: 67.06 | 3rd Qu.:1.0000 | 3rd Qu.: 2563883 | 3rd Qu.:1.258e+12 | 3rd Qu.:1.241e+17 | 3rd Qu.:2.287e+14 | 3rd Qu.:0.7365 | 3rd Qu.:0.0992 | 3rd Qu.:0.0028 | 3rd Qu.:0.0521 | 3rd Qu.:1.197e+09 | 3rd Qu.:1.535e+17 | 3rd Qu.:7.688e+17 | 3rd Qu.:1.300e+14 | 3rd Qu.:5.116e+13 | 3rd Qu.:1.647e+15 | 3rd Qu.: 0.8471 | 3rd Qu.: 0.0720 | 3rd Qu.: 0.4041 | 3rd Qu.:0.0306 | 3rd Qu.:0.0150 | 3rd Qu.: 0.3408 | 3rd Qu.:0.0026 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0e+00 | 3rd Qu.:0 | 3rd Qu.: 8604 | 3rd Qu.:0.2864 | 3rd Qu.:0.5983 | 3rd Qu.:0.7264 | 3rd Qu.:14.739 | 3rd Qu.: 7.444 | 3rd Qu.: 4.313 | 3rd Qu.: 1405891 | 3rd Qu.:3.835e+11 | 3rd Qu.:2.108e+16 | 3rd Qu.:7.100e+13 | 3rd Qu.:1.0132 | 3rd Qu.:0.1898 | 3rd Qu.:0.0074 | 3rd Qu.: 0.1622 | 3rd Qu.:6.281e+08 | 3rd Qu.:4.085e+16 | 3rd Qu.:2.124e+17 | 3rd Qu.:4.662e+13 | 3rd Qu.:2.294e+13 | 3rd Qu.:4.635e+14 | 3rd Qu.: 1.6894 | 3rd Qu.: 0.2874 | 3rd Qu.: 1.6383 | 3rd Qu.: 0.1056 | 3rd Qu.: 0.0611 | 3rd Qu.: 0.9441 | 3rd Qu.:0.0077 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.: 5322 | 3rd Qu.:0.3017 | 3rd Qu.:0.6051 | 3rd Qu.:0.7363 | 3rd Qu.:14.368 | 3rd Qu.: 7.143 | 3rd Qu.: 4.0015 | 3rd Qu.: 39.384 | 3rd Qu.: 61.21 | 3rd Qu.:1.0000 | 3rd Qu.: 2444647 | 3rd Qu.:1.136e+12 | 3rd Qu.:1.036e+17 | 3rd Qu.:2.211e+14 | 3rd Qu.:0.7633 | 3rd Qu.:0.1046 | 3rd Qu.:0.0029 | 3rd Qu.:0.0576 | 3rd Qu.:1.145e+09 | 3rd Qu.:1.387e+17 | 3rd Qu.:7.039e+17 | 3rd Qu.:1.258e+14 | 3rd Qu.:4.840e+13 | 3rd Qu.:1.513e+15 | 3rd Qu.: 0.9138 | 3rd Qu.: 0.0810 | 3rd Qu.: 0.4712 | 3rd Qu.:0.0344 | 3rd Qu.:0.0168 | 3rd Qu.: 0.3852 | 3rd Qu.:0.0029 | 3rd Qu.:0.0000 | 3rd Qu.:0.000 | 3rd Qu.:0.0000 | 3rd Qu.:0.000 | 3rd Qu.:0 | 3rd Qu.: 8213 | 3rd Qu.:0.2989 | 3rd Qu.:0.6085 | 3rd Qu.:0.7338 | 3rd Qu.:14.839 | 3rd Qu.: 7.393 | 3rd Qu.: 4.2305 | 3rd Qu.: 1411425 | 3rd Qu.:3.754e+11 | 3rd Qu.:1.981e+16 | 3rd Qu.:7.614e+13 | 3rd Qu.:1.0250 | 3rd Qu.:0.1896 | 3rd Qu.:0.0073 | 3rd Qu.: 0.1652 | 3rd Qu.:6.229e+08 | 3rd Qu.:4.167e+16 | 3rd Qu.:2.155e+17 | 3rd Qu.:4.987e+13 | 3rd Qu.:2.406e+13 | 3rd Qu.:4.685e+14 | 3rd Qu.: 1.7199 | 3rd Qu.: 0.2869 | 3rd Qu.: 1.696 | 3rd Qu.: 0.1089 | 3rd Qu.: 0.0639 | 3rd Qu.: 0.9822 | 3rd Qu.:0.0078 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.: 5277.11 | 3rd Qu.:0.3139 | 3rd Qu.:0.6152 | 3rd Qu.:0.7444 | 3rd Qu.:14.466 | 3rd Qu.: 7.099 | 3rd Qu.: 3.9472 | 3rd Qu.: 34.14 | 3rd Qu.: 47.706 | 3rd Qu.:1.0000 | 3rd Qu.: 2198054 | 3rd Qu.:9.254e+11 | 3rd Qu.:7.874e+16 | 3rd Qu.:1.902e+14 | 3rd Qu.:0.8274 | 3rd Qu.:0.1201 | 3rd Qu.:0.0033 | 3rd Qu.: 0.0759 | 3rd Qu.:1.030e+09 | 3rd Qu.:1.117e+17 | 3rd Qu.:5.691e+17 | 3rd Qu.:1.103e+14 | 3rd Qu.:4.242e+13 | 3rd Qu.:1.216e+15 | 3rd Qu.: 1.0766 | 3rd Qu.: 0.1077 | 3rd Qu.: 0.6657 | 3rd Qu.: 0.0462 | 3rd Qu.: 0.0222 | 3rd Qu.: 0.5005 | 3rd Qu.:0.0035 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0 | 3rd Qu.: 7378 | 3rd Qu.:0.3368 | 3rd Qu.:0.6307 | 3rd Qu.:0.7479 | 3rd Qu.:15.036 | 3rd Qu.: 7.290 | 3rd Qu.: 4.1043 | 3rd Qu.: 1379860 | 3rd Qu.:3.597e+11 | 3rd Qu.:1.752e+16 | 3rd Qu.:8.023e+13 | 3rd Qu.:1.0468 | 3rd Qu.:0.1909 | 3rd Qu.:0.0069 | 3rd Qu.: 0.1823 | 3rd Qu.:6.298e+08 | 3rd Qu.:3.960e+16 | 3rd Qu.:2.164e+17 | 3rd Qu.:5.226e+13 | 3rd Qu.:2.598e+13 | 3rd Qu.:4.794e+14 | 3rd Qu.: 1.7974 | 3rd Qu.: 0.3002 | 3rd Qu.: 1.901 | 3rd Qu.: 0.1243 | 3rd Qu.: 0.0725 | 3rd Qu.: 1.0608 | 3rd Qu.:0.0079 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.:0.0000 | 3rd Qu.: 5181.2 | 3rd 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Qu.:4.772e+14 | 3rd Qu.:1.779e+12 | 3rd Qu.: 0.608 | 3rd Qu.: 0.074 | 3rd Qu.:0.002 | 3rd Qu.: 0.032 | 3rd Qu.:6.932e+07 | 3rd Qu.:5.867e+14 | 3rd Qu.:2.249e+15 | 3rd Qu.:1.103e+12 | 3rd Qu.:4.860e+11 | 3rd Qu.:1.376e+13 | 3rd Qu.: 0.583 | 3rd Qu.: 0.038 | 3rd Qu.: 0.192 | 3rd Qu.: 0.020 | 3rd Qu.: 0.011 | 3rd Qu.: 0.195 | 3rd Qu.:0.004 | 3rd Qu.:0.000 | 3rd Qu.:0.000 | 3rd Qu.:0.000 | 3rd Qu.:0.000 | 3rd Qu.:0.000 | 3rd Qu.: 3583.6 | 3rd Qu.:0.599 | 3rd Qu.:0.773 | 3rd Qu.:0.859 | 3rd Qu.: 9.171 | 3rd Qu.: 4.796 | 3rd Qu.: 2.962 | 3rd Qu.: 1140.48 | 3rd Qu.: 41.434 | 3rd Qu.:0.05407 | 3rd Qu.:0.2123 | 3rd Qu.:0 | 3rd Qu.: 2.098 | 3rd Qu.:0.3560 | 3rd Qu.:1.0000 | NA | NA | NA | 3rd Qu.:0.2 | 3rd Qu.:1.9 | 3rd Qu.:1.4 | 3rd Qu.:2 | 3rd Qu.:2.401 | 3rd Qu.: 4.709e-10 | 3rd Qu.:0.337722 | 3rd Qu.:0.2628045 | 3rd Qu.:-0.7773 | 3rd Qu.: 3.4233 | 3rd Qu.: 6.750e-10 | 3rd Qu.:0.32442 | 3rd Qu.:0.269623 | 3rd Qu.:-0.6866 | 3rd Qu.: 3.1626 | 3rd Qu.: 1.301e-10 | 3rd Qu.:0.164254 | 3rd Qu.:0.0715205 | 3rd Qu.: -0.55303 | 3rd Qu.: 2.95817 | 3rd Qu.: 4.920e-10 | 3rd Qu.:0.304156 | 3rd Qu.:0.251224 | 3rd Qu.:-0.5877 | 3rd Qu.: 3.0455 | 3rd Qu.: 1632553 | 3rd Qu.: 847645 | 3rd Qu.: 1577760 | 3rd Qu.:0.4363 | 3rd Qu.:-0.00172 | 3rd Qu.:0.14024 | 3rd Qu.:-0.1177243 | 3rd Qu.:0.22674 | 3rd Qu.:9.420e-02 | 3rd Qu.:0.42618 | 3rd Qu.:-0.00113 | 3rd Qu.:0.09062 | 3rd Qu.:-0.11395 | 3rd Qu.:0.19424 | 3rd Qu.: 0.091062 | 3rd Qu.:0.42462 | 3rd Qu.:-0.00012 | 3rd Qu.:0.13623 | 3rd Qu.:-0.007293 | 3rd Qu.:0.15861 | 3rd Qu.: 0.01579 | 3rd Qu.:0.17967 | 3rd Qu.:-0.00043 | 3rd Qu.:0.05314 | 3rd Qu.:-0.106279 | 3rd Qu.:0.17112 | 3rd Qu.: 0.084687 | 3rd Qu.:0.40525 | 3rd Qu.:-0.23695 | 3rd Qu.:0.17675 | 3rd Qu.:0.000e+00 | 3rd Qu.:0.000e+00 | 3rd Qu.: 0.5041 | 3rd Qu.:-0.00096 | 3rd Qu.:0.13851 | 3rd Qu.:2.5 | 3rd Qu.:7.1 | 3rd Qu.:0.5 | |
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:6.2210 | Max. :3.2789 | Max. :0.4408 | Max. :56.1583 | Max. :5.444e+11 | Max. :3.703e+22 | Max. :2.191e+23 | Max. :2.480e+18 | Max. :1.500e+18 | Max. :3.396e+19 | Max. :55.3711 | Max. :671.2784 | Max. :2712.8508 | Max. :54.3712 | Max. :53.1798 | Max. :257.4787 | Max. :1.0142 | Max. :0.0892 | Max. :0.9735 | Max. :0.1136 | Max. :0.1108 | Max. :0.0036 | Max. :40001 | Max. :0.9577 | Max. :0.9973 | Max. :1.0000 | Max. :57.158 | Max. :23.337 | Max. :18.8283 | Max. :308.854 | Max. :1364.02 | Max. :3.0000 | Max. :488904295 | Max. :5.504e+16 | Max. :1.774e+24 | Max. :8.822e+19 | Max. :3.2426 | Max. :0.8686 | Max. :0.0658 | Max. :8.7118 | Max. :2.080e+12 | Max. :8.316e+23 | Max. :2.328e+24 | Max. :5.101e+19 | Max. :2.620e+19 | Max. :4.918e+20 | Max. :15.7489 | Max. :17.9810 | Max. :216.0913 | Max. :5.7801 | Max. :4.1458 | Max. :35.0370 | Max. :0.0797 | Max. :0.0012 | Max. :0.005 | Max. :0.0016 | Max. :0.001 | Max. :0 | Max. :170502 | Max. :0.9550 | Max. :0.9933 | Max. :1.0000 | Max. :57.498 | 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:7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA’s :7026 | NA | NA | NA | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA’s :8280 | NA | NA | NA | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA’s :9341 | NA | NA | NA | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA’s :10246 | NA | NA | NA | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA’s :11011 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :77 | NA’s :77 | NA | NA | NA | NA | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :139 | NA’s :77 | NA’s :77 | NA | NA | NA |
Ze względu na bardzo dużą liczbę zmiennych, korelacje pomiędzy wszystkimi parami bylyby trudne do wizualizacji i oceny. Z tego powodu dane zostały wyselekcjonowane i podzielone na grupy, w ramach których sprawdzono korelacje. Wszystkie poniższe wykresy zostały wygenerowane na wierszach w których w analizowanych kolumnach nie było wartości NA.
W pierwszej kolejności wybrano grupę zmiennych opisujacych podstawowe wlaściwości ligandu z pominięciem kolumn part_XX, które będą badane osobno.
M = select(data3, local_volume, local_electrons, local_mean, local_std, local_max, local_skewness, local_parts,solvent_mask_count, void_mask_count, modeled_mask_count, solvent_ratio, local_res_atom_count, local_res_atom_non_h_electron_sum, local_res_atom_non_h_count)
corrplot(cor(M), method = "circle", type = "upper")
Kolejny krok analizy dotyczyć będzie kolumn rozpoczynających się od frazy “part_”. Z powodu liczby tych kolumn należy wprowadzić pewne podziały. Naturalnym wydaje się podział danch według progów odcięcia. Pierwszy wykres przedstawia wszystkie kolumny dla progu odcięcia równego 0, czyli takie zaczynające się od “part_00”. Dla czytelności usunięto tę fazę z oznaczeń kolumn.
str <- 'part_00'
M = select(data3, starts_with(str))
M <- M[complete.cases(M),]
names(M) <- substr(names(M), nchar(str)+2, 100)
corrplot(cor(M), method = "circle", type = "upper", tl.cex = 0.6)
Na wykresie widać dużą liczbę mocnych korelacji dodatnich, występują także pojedyncze korlacje ujemne.
Korelacje dla kolejnych progów odcięcia są w większości podobne, dlatego poniżej przedstawione zostały w sposób uproszczony:
strs <- c("part_01", "part_02", "part_03", "part_04", "part_05", "part_06", "part_07", "part_08", "part_09")
layout(matrix(1:9, ncol = 3))
plots <- list()
for (str in strs) {
M = select(data3, starts_with(str))
M <- M[complete.cases(M),]
names(M) <- substr(names(M), nchar(str)+2, 100)
plots$str <- corrplot(cor(M), method = "circle", type = "upper", cl.pos = "n", tl.pos = "n", title = str, mar=c(0,0,1,0))
}
Analizowany zbiór zawiera bardzo dużą liczbę klas. Poniżej zaprezentowano wyliczenie tej liczby oraz pierwsze 20 klas posortowane wg liczby ligandów które do nich należą.
classes <- table(select(data3,res_name))
classes <- sort(classes, decreasing = TRUE)
length(classes)
## [1] 2696
h <- head(classes, 20)
kable(t(as.matrix(h)))
| SO4 | ZN | GOL | CA | MG | CL | NAG | PO4 | HEM | EDO | MN | ACT | FAD | ADP | FE | K | ATP | CU | PEG | NAP |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1183 | 849 | 778 | 661 | 402 | 400 | 396 | 301 | 296 | 274 | 182 | 167 | 157 | 138 | 120 | 119 | 106 | 104 | 90 | 86 |
Poniższy wykres prezentuje rozkład danych wg liczby atomów i elektronów. Oś X przedstawia elektrony, a Y - atomy.
theme_empty <- theme(axis.line=element_blank(),
axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.margin = unit(c(0,0,0,0), "lines"))
theme_scale_only <- theme(axis.line=element_blank(),
axis.text.x=element_text(size=18),
axis.text.y=element_text(size=18, angle=90),
axis.title.x=element_blank(),
axis.title.y=element_blank(),
legend.position="none",
panel.background=element_rect(fill = '#5e4fa2', colour = '#5e4fa2'),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.margin = unit(c(0,0,0,0), "lines"))
a <- ggplot(data3, aes(x = local_res_atom_non_h_count)) + stat_bin(binwidth = 1, fill = 'red', color= 'black') + coord_flip() + theme_empty + xlim(c(0, 100))
e <- ggplot(data3, aes(x = local_res_atom_non_h_electron_sum)) + stat_bin(binwidth = 6, fill = 'red', color= 'black') + theme_empty+ xlim(c(0, 650))
d <- ggplot(data3, aes(x = local_res_atom_non_h_electron_sum, y = local_res_atom_non_h_count)) + stat_density2d(aes(fill = ..level..), geom="polygon") + scale_fill_gradientn(colours=rev(rainbow(200, start=0, end=0.68))) + guides(alpha=FALSE) + scale_x_continuous(limits=c(0, 650), breaks=seq(100, 600, 100), expand = c(0, 0)) + scale_y_continuous(limits=c(0, 100), breaks=seq(20, 100, 20), expand = c(0, 0)) + theme_scale_only
blankPlot <- ggplot()+geom_blank(aes(1,1))+
theme(plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank())
grid.arrange(e, blankPlot, d, a, ncol=2, nrow=2, widths=c(4, 1), heights=c(1, 4))
## Warning: Removed 6 rows containing non-finite values (stat_density2d).
Jako miara niezgodności liczby atomów (local_res_atom_non_h_count vs dict_atom_non_h_count) i elektronów (local_res_atom_non_h_count vs dict_atom_non_h_count) dla poszczególnych klas użyta zostanie średnia.
Przygotowanie danych
M = select(data3, res_name, local_res_atom_non_h_count, dict_atom_non_h_count, local_res_atom_non_h_electron_sum, dict_atom_non_h_electron_sum)
M$electronsDiff = abs(M$local_res_atom_non_h_count - M$dict_atom_non_h_count)
M$atomsDiff = abs(M$local_res_atom_non_h_electron_sum - M$dict_atom_non_h_electron_sum)
Niezgodność liczby elektronów
ME <- aggregate(M$electronsDiff, by=list(Category=M$res_name), FUN=mean)
colnames(ME) <- c("group", "mean")
ME <- ME[order(ME$mean, decreasing = TRUE),]
kable(head(ME, 20))
| group | mean | |
|---|---|---|
| 2087 | PEU | 66.50000 |
| 2068 | PC1 | 33.33333 |
| 1017 | CPQ | 33.00000 |
| 1603 | JEF | 33.00000 |
| 2564 | VV7 | 33.00000 |
| 1736 | M0E | 32.00000 |
| 2172 | PTY | 31.00000 |
| 1690 | LI1 | 29.50000 |
| 1566 | IP9 | 27.00000 |
| 2083 | PEF | 27.00000 |
| 1712 | LP5 | 26.00000 |
| 962 | CDL | 24.77778 |
| 1711 | LP4 | 23.00000 |
| 1940 | NKP | 23.00000 |
| 2260 | S0N | 23.00000 |
| 995 | CM5 | 21.00000 |
| 1777 | MDS | 21.00000 |
| 1941 | NKQ | 21.00000 |
| 2435 | THF | 21.00000 |
| 2394 | T5C | 20.00000 |
Niezgodność liczby atomów
ME <- aggregate(M$atomsDiff, by=list(Category=M$res_name), FUN=mean)
colnames(ME) <- c("group", "mean")
ME <- ME[order(ME$mean, decreasing = TRUE),]
kable(head(ME, 20))
| group | mean | |
|---|---|---|
| 2087 | PEU | 443.0000 |
| 1017 | CPQ | 225.0000 |
| 2564 | VV7 | 224.0000 |
| 1603 | JEF | 213.0000 |
| 2068 | PC1 | 211.6667 |
| 1736 | M0E | 196.0000 |
| 2083 | PEF | 188.0000 |
| 2172 | PTY | 186.0000 |
| 1566 | IP9 | 185.0000 |
| 1690 | LI1 | 183.0000 |
| 1940 | NKP | 161.0000 |
| 1712 | LP5 | 160.0000 |
| 2394 | T5C | 153.0000 |
| 2260 | S0N | 150.0000 |
| 279 | 2J8 | 149.0000 |
| 1941 | NKQ | 149.0000 |
| 962 | CDL | 148.8889 |
| 995 | CM5 | 146.0000 |
| 1777 | MDS | 142.0000 |
| 1711 | LP4 | 140.0000 |
M = select(data3, starts_with('part_01'))
ggplot(M, aes(x=c(M[,1]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,1]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,1])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_electron_sum")
ggplot(M, aes(x=c(M[,2]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,2]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,2])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_volume_sum")
ggplot(M, aes(x=c(M[,3]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,3]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,3])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_blob_parts")
M <- M[complete.cases(M),]
ggplot(M, aes(x=c(M[,4]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,4]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,4])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O3")
ggplot(M, aes(x=c(M[,5]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,5]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,5])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O4")
ggplot(M, aes(x=c(M[,6]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,6]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,6])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O5")
ggplot(M, aes(x=c(M[,7]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,7]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,7])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_FL")
ggplot(M, aes(x=c(M[,8]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,8]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,8])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O3_norm")
ggplot(M, aes(x=c(M[,9]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,9]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,9])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O4_norm")
ggplot(M, aes(x=c(M[,10]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,10]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,10])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_O5_norm")
ggplot(M, aes(x=c(M[,11]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,11]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,11])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_FL_norm")
ggplot(M, aes(x=c(M[,12]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,12]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,12])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1")
ggplot(M, aes(x=c(M[,13]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,13]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,13])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2")
ggplot(M, aes(x=c(M[,14]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,14]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,14])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3")
ggplot(M, aes(x=c(M[,15]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,15]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,15])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4")
ggplot(M, aes(x=c(M[,16]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,16]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,16])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5")
ggplot(M, aes(x=c(M[,17]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,17]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,17])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6")
ggplot(M, aes(x=c(M[,18]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,18]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,18])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1_norm")
ggplot(M, aes(x=c(M[,19]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,19]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,19])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2_norm")
ggplot(M, aes(x=c(M[,20]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,20]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,20])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3_norm")
ggplot(M, aes(x=c(M[,21]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,21]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,21])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4_norm")
ggplot(M, aes(x=c(M[,22]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,22]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,22])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5_norm")
ggplot(M, aes(x=c(M[,23]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,23]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,23])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6_norm")
ggplot(M, aes(x=c(M[,24]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,24]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,24])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I1_scaled")
ggplot(M, aes(x=c(M[,25]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,25]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,25])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I2_scaled")
ggplot(M, aes(x=c(M[,26]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,26]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,26])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I3_scaled")
ggplot(M, aes(x=c(M[,27]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,27]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,27])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I4_scaled")
ggplot(M, aes(x=c(M[,28]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,28]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,28])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I5_scaled")
ggplot(M, aes(x=c(M[,29]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,29]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,29])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_I6_scaled")
ggplot(M, aes(x=c(M[,30]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,30]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,30])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_M000")
ggplot(M, aes(x=c(M[,31]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,31]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,31])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E3_E1")
ggplot(M, aes(x=c(M[,32]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,32]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,32])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E2_E1")
ggplot(M, aes(x=c(M[,33]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,33]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,33])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_E3_E2")
ggplot(M, aes(x=c(M[,34]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,34]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,34])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E1")
ggplot(M, aes(x=c(M[,35]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,35]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,35])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E2")
ggplot(M, aes(x=c(M[,36]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,36]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,36])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_shape_sqrt_E3")
ggplot(M, aes(x=c(M[,37]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,37]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,37])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O3")
ggplot(M, aes(x=c(M[,38]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,38]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,38])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O4")
ggplot(M, aes(x=c(M[,39]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,39]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,39])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O5")
ggplot(M, aes(x=c(M[,40]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,40]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,40])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_FL")
ggplot(M, aes(x=c(M[,41]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,41]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,41])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O3_norm")
ggplot(M, aes(x=c(M[,42]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,42]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,42])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O4_norm")
ggplot(M, aes(x=c(M[,43]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,43]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,43])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_O5_norm")
ggplot(M, aes(x=c(M[,44]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,44]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,44])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_FL_norm")
ggplot(M, aes(x=c(M[,45]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,45]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,45])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1")
ggplot(M, aes(x=c(M[,46]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,46]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,46])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2")
ggplot(M, aes(x=c(M[,47]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,47]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,47])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3")
ggplot(M, aes(x=c(M[,48]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,48]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,48])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4")
ggplot(M, aes(x=c(M[,49]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,49]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,49])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5")
ggplot(M, aes(x=c(M[,50]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,50]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,50])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6")
ggplot(M, aes(x=c(M[,51]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,51]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,51])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1_norm")
ggplot(M, aes(x=c(M[,52]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,52]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,52])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2_norm")
ggplot(M, aes(x=c(M[,53]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,53]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,53])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3_norm")
ggplot(M, aes(x=c(M[,54]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,54]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,54])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4_norm")
ggplot(M, aes(x=c(M[,55]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,55]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,55])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5_norm")
ggplot(M, aes(x=c(M[,56]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,56]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,56])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6_norm")
ggplot(M, aes(x=c(M[,57]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,57]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,57])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I1_scaled")
ggplot(M, aes(x=c(M[,58]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,58]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,58])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I2_scaled")
ggplot(M, aes(x=c(M[,59]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,59]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,59])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I3_scaled")
ggplot(M, aes(x=c(M[,60]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,60]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,60])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I4_scaled")
ggplot(M, aes(x=c(M[,61]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,61]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,61])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I5_scaled")
ggplot(M, aes(x=c(M[,62]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,62]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,62])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_I6_scaled")
ggplot(M, aes(x=c(M[,63]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,63]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,63])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_M000")
ggplot(M, aes(x=c(M[,64]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,64]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,64])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E3_E1")
ggplot(M, aes(x=c(M[,65]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,65]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,65])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E2_E1")
ggplot(M, aes(x=c(M[,66]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,66]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,66])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_E3_E2")
ggplot(M, aes(x=c(M[,67]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,67]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,67])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E1")
ggplot(M, aes(x=c(M[,68]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,68]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,68])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E2")
ggplot(M, aes(x=c(M[,69]))) + geom_density()+ geom_vline(aes(xintercept=mean(c(M[,69]), na.rm=T)), color="red", linetype="dashed", size=1)+geom_text(aes(x=mean(c(M[,69])), y=0, vjust=-1, hjust=-0.2, label = round(..x.., digits=4))) + labs(x="part_01_density_sqrt_E3")
Do przewidywania liczby atomów i elektronów zdecydowano się wykorzystać kolumny z grupy part_00. Wybrano te kolumny które mają największe korelacje z kolumną part_00_blob_electron_sum
M = select(data3, one_of("local_res_atom_non_h_electron_sum", "part_00_blob_electron_sum", "part_00_shape_O3", "part_00_shape_M000", "part_00_shape_sqrt_E1", "part_00_shape_sqrt_E2", "part_00_shape_sqrt_E3", "part_00_density_O3", "part_00_density_M000"))
M <- M[complete.cases(M),]
ctrl <- trainControl(method = "repeatedcv", number = 3)
fit <- train(local_res_atom_non_h_electron_sum ~ ., data = M, method = "glm", trControl = ctrl)
predicted <- predict(fit, newdata = M)
acctual <- M$local_res_atom_non_h_electron_sum
Miary R^2 i RMSE dla predykcji liczby elektronów
cor(acctual,predicted)^2
## [1] 0.5641556
rmse(predicted,acctual)
## [1] 64.31729
M = select(data3, one_of("local_res_atom_non_h_count", "part_00_blob_electron_sum", "part_00_shape_O3", "part_00_shape_M000", "part_00_shape_sqrt_E1", "part_00_shape_sqrt_E2", "part_00_shape_sqrt_E3", "part_00_density_O3", "part_00_density_M000"))
M <- M[complete.cases(M),]
ctrl <- trainControl(method = "repeatedcv", number = 3)
fit <- train(local_res_atom_non_h_count ~ ., data = M, method = "glm", trControl = ctrl)
predicted <- predict(fit, newdata = M)
acctual <- M$local_res_atom_non_h_count
Miary R^2 i RMSE dla predykcji liczby atomów
cor(acctual,predicted)^2
## [1] 0.5648851
rmse(predicted,acctual)
## [1] 9.544055
M = select(data3, matches('res_name|part_00.*|local_electrons|local_mean|local_std|local_max|local_skewness|local_parts,solvent_mask_count|void_mask_count|modeled_mask_count|solvent_ratio'))
M <- M[complete.cases(M),]
classes <- table(select(M,res_name))
classes <- sort(classes, decreasing = TRUE)
top <- head(classes, 20)
Mf <- filter(M, is.element(res_name, names(top)))
Mf$res_name <- factor(Mf$res_name)
inTraining <- createDataPartition(y = Mf$res_name, p = .5, list = FALSE)
training <- Mf[ inTraining,]
testing <- Mf[-inTraining,]
ctrl <- trainControl(method = "repeatedcv", number = 2, repeats = 5)
fit <- train(res_name ~ part_00_blob_electron_sum+part_00_blob_volume_sum+part_00_blob_parts+part_00_shape_O3+part_00_shape_O4+part_00_shape_O5+part_00_shape_FL+part_00_shape_O3_norm+part_00_shape_O4_norm+part_00_shape_O5_norm+part_00_shape_FL_norm+part_00_shape_I1+part_00_shape_I2+part_00_shape_I3+part_00_shape_I4+part_00_shape_I5+part_00_shape_I6+part_00_shape_I1_norm+part_00_shape_I2_norm+part_00_shape_I3_norm+part_00_shape_I4_norm+part_00_shape_I5_norm+part_00_shape_I6_norm+part_00_shape_I1_scaled+part_00_shape_I2_scaled+part_00_shape_I3_scaled+part_00_shape_I4_scaled+part_00_shape_I5_scaled+part_00_shape_I6_scaled+part_00_shape_M000+part_00_shape_E3_E1+part_00_shape_E2_E1+part_00_shape_E3_E2+part_00_shape_sqrt_E1+part_00_shape_sqrt_E2+part_00_shape_sqrt_E3+part_00_density_O3+part_00_density_O4+part_00_density_O5+part_00_density_FL+part_00_density_O3_norm+part_00_density_O4_norm+part_00_density_O5_norm+part_00_density_FL_norm+part_00_density_I1+part_00_density_I2+part_00_density_I3+part_00_density_I4+part_00_density_I5+part_00_density_I6+part_00_density_I1_norm+part_00_density_I2_norm+part_00_density_I3_norm+part_00_density_I4_norm+part_00_density_I5_norm+part_00_density_I6_norm+part_00_density_I1_scaled+part_00_density_I2_scaled+part_00_density_I3_scaled+part_00_density_I4_scaled+part_00_density_I5_scaled+part_00_density_I6_scaled+part_00_density_M000+part_00_density_E3_E1+part_00_density_E2_E1+part_00_density_E3_E2+part_00_density_sqrt_E1+part_00_density_sqrt_E2+part_00_density_sqrt_E3+local_electrons+local_mean+local_std+local_max+local_skewness+void_mask_count+modeled_mask_count+solvent_ratio, data = training, method = "rf", trControl = ctrl)
predicted <- predict(fit, newdata = testing)
confusionMatrix(data = predicted, testing$res_name)
## Confusion Matrix and Statistics
##
## Reference
## Prediction ACT ADP ATP CA CL CU EDO FAD FE GOL HEM K MG MN NAG NAP
## ACT 28 0 0 1 6 1 7 0 1 12 0 0 3 0 1 0
## ADP 0 30 14 7 0 0 0 2 2 0 1 0 2 3 3 4
## ATP 0 12 14 2 0 0 0 0 0 0 1 0 2 1 0 0
## CA 1 0 2 172 6 3 1 0 12 9 4 7 25 19 2 0
## CL 6 1 0 8 79 0 3 0 1 4 0 8 5 2 0 0
## CU 0 0 0 1 0 5 0 0 1 0 0 0 0 0 0 0
## EDO 7 0 0 4 8 0 39 0 1 33 0 1 5 1 2 0
## FAD 0 1 2 0 0 0 0 54 0 0 2 0 0 0 4 3
## FE 0 0 0 0 0 0 0 0 4 0 0 0 0 2 0 0
## GOL 20 5 0 11 15 1 61 1 1 247 1 3 42 2 27 0
## HEM 0 0 3 0 0 2 0 3 2 0 129 0 1 3 4 1
## K 0 1 0 0 3 0 0 0 0 0 0 3 0 0 0 0
## MG 2 3 1 18 8 3 3 1 1 16 2 4 60 3 3 0
## MN 0 1 1 5 0 1 0 0 1 0 2 0 1 9 0 0
## NAG 0 11 8 7 2 1 0 5 1 15 2 3 13 3 143 12
## NAP 0 2 2 2 0 0 0 8 0 0 0 0 0 2 1 22
## PEG 0 0 0 0 1 0 2 0 1 3 0 0 1 0 0 0
## PO4 0 0 1 2 1 0 0 0 1 1 0 0 0 0 0 0
## SO4 14 0 0 53 53 3 10 0 5 30 0 22 29 7 4 0
## ZN 0 1 5 28 1 30 1 2 24 1 1 2 5 31 1 0
## Reference
## Prediction PEG PO4 SO4 ZN
## ACT 0 4 7 0
## ADP 0 0 0 4
## ATP 0 0 0 1
## CA 0 11 18 30
## CL 1 7 9 0
## CU 0 0 0 4
## EDO 4 2 4 0
## FAD 0 0 2 0
## FE 0 0 0 3
## GOL 22 9 15 5
## HEM 0 0 0 8
## K 0 0 0 0
## MG 0 4 10 11
## MN 0 2 0 3
## NAG 8 3 11 6
## NAP 0 0 0 1
## PEG 6 0 0 0
## PO4 0 6 11 0
## SO4 1 93 486 26
## ZN 0 7 9 313
##
## Overall Statistics
##
## Accuracy : 0.562
## 95% CI : (0.5449, 0.579)
## No Information Rate : 0.1769
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.511
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: ACT Class: ADP Class: ATP Class: CA Class: CL
## Sensitivity 0.358974 0.441176 0.264151 0.53583 0.43169
## Specificity 0.986613 0.986965 0.994130 0.94948 0.98230
## Pos Pred Value 0.394366 0.416667 0.424242 0.53416 0.58955
## Neg Pred Value 0.984467 0.988191 0.988026 0.94980 0.96705
## Prevalence 0.023708 0.020669 0.016109 0.09757 0.05562
## Detection Rate 0.008511 0.009119 0.004255 0.05228 0.02401
## Detection Prevalence 0.021581 0.021884 0.010030 0.09787 0.04073
## Balanced Accuracy 0.672794 0.714071 0.629141 0.74265 0.70700
## Class: CU Class: EDO Class: FAD Class: FE Class: GOL
## Sensitivity 0.100000 0.30709 0.71053 0.067797 0.66577
## Specificity 0.998148 0.97724 0.99564 0.998452 0.91744
## Pos Pred Value 0.454545 0.35135 0.79412 0.444444 0.50615
## Neg Pred Value 0.986276 0.97232 0.99317 0.983237 0.95575
## Prevalence 0.015198 0.03860 0.02310 0.017933 0.11277
## Detection Rate 0.001520 0.01185 0.01641 0.001216 0.07508
## Detection Prevalence 0.003343 0.03374 0.02067 0.002736 0.14833
## Balanced Accuracy 0.549074 0.64216 0.85309 0.533125 0.79160
## Class: HEM Class: K Class: MG Class: MN Class: NAG
## Sensitivity 0.88966 0.0566038 0.30928 0.102273 0.73333
## Specificity 0.99141 0.9987643 0.96996 0.994691 0.96414
## Pos Pred Value 0.82692 0.4285714 0.39216 0.346154 0.56299
## Neg Pred Value 0.99489 0.9847700 0.95728 0.975797 0.98287
## Prevalence 0.04407 0.0161094 0.05897 0.026748 0.05927
## Detection Rate 0.03921 0.0009119 0.01824 0.002736 0.04347
## Detection Prevalence 0.04742 0.0021277 0.04650 0.007903 0.07720
## Balanced Accuracy 0.94054 0.5276840 0.63962 0.548482 0.84873
## Class: NAP Class: PEG Class: PO4 Class: SO4 Class: ZN
## Sensitivity 0.523810 0.142857 0.040541 0.8351 0.75422
## Specificity 0.994458 0.997537 0.994589 0.8708 0.94817
## Pos Pred Value 0.550000 0.428571 0.260870 0.5813 0.67749
## Neg Pred Value 0.993846 0.989011 0.956535 0.9609 0.96393
## Prevalence 0.012766 0.012766 0.044985 0.1769 0.12614
## Detection Rate 0.006687 0.001824 0.001824 0.1477 0.09514
## Detection Prevalence 0.012158 0.004255 0.006991 0.2541 0.14043
## Balanced Accuracy 0.759134 0.570197 0.517565 0.8529 0.85120